Dilshan Asintha Wittahachchi 
Master’s Thesis  
Reducing Design Iteration Cycles in Design for Additive Manufacturing 
(DfAM) of Components for Hydrofoils & Drones 
 
 
 
 
 
  
Vaasa 2025 
School of Technology and Innovations 
Master’s thesis in Design for Additive Manufacturing  
Industrial Engineering and Management   
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ACKNOWLEDGEMENT 
First, I would like to thank my supervisor for their continuous guidance, patience, and 
treasured advice throughout the period of my master’s degree program and this thesis. 
His expert guidance and professional direction were key in defining the course and 
extent of my research. I am deeply indebted to the Networked Value Systems research 
group for providing me with the opportunity, tools, and academic environment to carry 
out research work seamlessly. The motivational atmosphere and constructive feedback 
I gained greatly enriched my research experience. 
 
My sincerest thanks are due to my parents and family, whose moral support, motivation, 
and unconditional love have been the foundation of all my success. Their trust in me has 
been my strength throughout my academic and professional career. I also want to thank 
my mentors, friends, and colleagues, who helped me with valuable discussions, 
encouragement, and support at various stages of this work. They made this experience 
more rewarding and enriching with their friendship and encouragement.  
 
Finally, I thank everyone who has named and unnamed, who directly or indirectly made 
this thesis possible. This would not have been possible without your support and 
kindness.  
 
Thank you all. 
 
  
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UNIVERSITY OF VAASA 
School of Technology and Innovations 
Author: Dilshan Asintha Wittahachchi 
Title of the thesis:  Master’s Thesis: Reducing Design Iteration Cycles in Design for 
Additive Manufacturing (DfAM) of Components for Hydrofoils & 
Drones 
Degree: Master of Industrial Engineering and Management 
Discipline: Industrial Systems Analytics 
Supervisor: Dr. Rayko Toshev  
Year: 2025 Pages: 112 
ABSTRACT:  
The purpose of the thesis is to reduce the design iteration cycle time while preserving design 
constraints and desired functionality, enabling faster product development in high performance 
applications across industries. The research aims to contribute to the Design phase of Additive 
Manufacturing (AM) practices for candidate 3D models from industries. The study is motivated 
by the inefficiency and time consuming nature of the current design process for AM components 
in specialised applications, which often involves multiple cycles of redesign, simulation, and 
prototyping that squander precious resources and time. 
The Framework of the thesis introduces a general framework to reduce design iteration cycles in 
Design for Additive Manufacturing (DfAM), which optimises the workpath using a generative 
design software approach. The framework's architecture is built upon four key pillars: design 
optimisation, TO, simulation validation, and manufacturing preparation. It integrates multiple 
specialised software tools for advanced computational modeling. 
The research employs a mono-methods approach focused on the practical implementation of a 
workpath that integrates design, computational modelling with different software, and results 
evaluation to reduce design iterations and advance DfAM techniques. It adopts a hybrid 
deductive and inductive research approach to gain in-depth insights into DfAM.  
The developed framework demonstrated a considerable reduction in design iterations across all 
case studies. This significant decrease is attributed to the framework's effectiveness in design 
refinements and finalisation phases. Despite these advancements, the framework acknowledges 
limitations such as high computational resource requirements, multi-material limitations, size 
constraints, and simulation fidelity gaps 
KEYWORDS: Addive manufacturing, DfAM, Design iterations, TO, Lattice Integration. 
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Contents  
ACKNOWLEDGEMENT 2 
1 Introduction 10 
1.1 Motivation for the study 10 
1.2 Field of Science 11 
1.3 Additive Manufacturing Workflow 13 
1.4 Research Onion Framework 15 
1.5 Research Philosophy – Pragmatism 16 
1.6 Research approach – Deductive and Inductive 16 
1.6.1 Research Strategy 17 
1.6.2 Time Horizon – Cross Sectional 18 
1.6.3 Data 18 
1.6.4 Research gap and justification for the current studies 19 
1.7 Research Objectives and Research Questions 22 
1.8 Ethical consideration – use of AI tools 24 
1.9 Thesis Structure 25 
2 Literature Review 26 
2.1 Integration of simulation tools with AM workflow 26 
2.1.1 Software platforms could integrate for Design-to-Manufacturing workflows
 27 
2.2 Additive Manufacturing Technologies: Capabilities and Constraints for 
Aerospace and Marine applications 28 
2.2.1 Selective Lase Sintering (SLS): Capabilities and Limitations 28 
2.2.2 Fused Deposition Modeling (FDM): Capabilities and Limitations 29 
2.2.3 Steriolightography (SLA): Capabilities and Limitations 30 
2.2.4 Strategies for reducing design iterations in Aerospace and Marine 
Industries. 31 
2.2.5 Digital Twin approaches Virtual Validations 31 
2.2.6 Parametric optimization and Design space exploration 32 
2.2.7 Process parameter optimization Iteration reduction 33 
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2.3 Comparative studies of AM technologies for Aerospace and Marine Applications
 33 
2.3.1 Iteration efficiency across development phases 34 
2.4 Bio Mimicking Compliant Mechanisms 35 
2.4.1 Design and actuation of a biomimetic Beetle wing 37 
2.4.2 Additive manufacturing braces for Biomemetic compliant mechanisms 39 
2.4.3 Design to Manufacturing approaches for Biomimetic Mechanisms 40 
2.5 Comparative Study of Additive Manufacturing Methods 41 
2.6 FDM Surface roughness evaluation 41 
2.7 Statistical Analysis for AM technologies comparison 43 
2.7.1 Analysis in Python 43 
2.7.2 Plots generation 44 
2.7.3 Efficiency/ Performance advantages of Biomimetic Compliant Mechanisms
 46 
3 Methodology 47 
3.1 Methodological Choice 47 
3.2 Case study/ Use case selection 47 
3.3 Design/ Redesign process 48 
3.3.1 Design for Additive Manufacturing Integration 48 
3.3.2 Software Integration 48 
3.4 Simulation and Analysis 49 
3.4.1 Process Simulation for Additive Manufacturing 49 
3.4.2 Multi-Scale and Multi-Material Topology Optimization 50 
3.5 Literature/ Statistical based data anlysis 51 
3.6 Evaluation Criteria 51 
3.7 Experimental Methodology 52 
3.7.1 Additive manufacturing prototyping 53 
3.8 Validation Methodology 55 
3.8.1 Digital Validation 56 
4 Design, Optimization, Simulation, and Analysis 57 
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4.1 Software Integration 57 
4.2 Design Optimization 59 
4.3 Topology Optimization 59 
4.3.1 Aerofoil Flap Actuator 60 
4.4 Lattice Structures 61 
4.4.1 Lattice Generation Methodology 62 
4.5 Multi-Objective Optimization 64 
4.6 Components Design and Development 65 
4.6.1 Structural Components 65 
4.6.2 Functional Components 65 
4.6.3 Biomimetic Compliant Mechanisms 66 
4.7 Case Studies 68 
4.7.1 Satellite structural component optimization 69 
4.7.2 Structural optimization with full lattice integration 74 
4.7.3 Hydrofoil Optimization 76 
4.7.4 Drone Wing structural optimization 78 
4.8 Biomimetic Design Implementation 84 
5 Evaluation and Discussion 88 
5.1 Performance Analysis 88 
5.2 Qualitative analysis 93 
5.2.1 Framework effectiveness on aerospace applications 93 
5.2.2 Framework effectiveness on marine applications 93 
5.2.3 Biomimetic Applications 94 
5.3 Framework Limitations and Challenges 95 
6 Future Research Directions 96 
6.1 Technical Enhancement 96 
6.2 Economic and Sustainability impact 97 
6.3 Technical research needs 98 
7 Conclusion 99 
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8 References 101 
9 Appendices 107 
 
 
Figures  
Figure 1. Field of science for thesis development 11 
Figure 2. Optimization driven design process of AM practicing design iteration and 
validation. 14 
Figure 3. Adapted Research Onion for the study 15 
Figure 4. Innovation inspiration and abstraction by nature 36 
Figure 5. Biomimetic Beetle wing modeled in Onshape 38 
Figure 6. Surface roughness of AM technologies 44 
Figure 7. Dimensional Error of AM technologies 45 
Figure 8. Tensile Strength of AM technologies 45 
Figure 9. Evaluation matrics defined to evaluate the performance 52 
Figure 10. Integrated validation methodology to validate experimental results 55 
Figure 11. Additive Manufacturing workpath 57 
Figure 12. Topology Optimization process to adapt in geometry optimization 59 
Figure 13. Flap actuator prior to perform TO 60 
Figure 14. Aerofoil flap actuator – Topology Optimized in Altair HyperMesh 2024 61 
Figure 15. Lattice cell orientation in generative design software platform Altair Inspire
 62 
Figure 16. Multi-objective optimization in Additive Manufacturing 64 
Figure 17. Biomimetic mechanism developmemt cycle 67 
Figure 18. Cubesat Bus Assembly structure, a pre modelled CAD body 69 
Figure 19. Lattice integration for the satellite structure in Altair Inspire (2024) 70 
Figure 20. Lattice integration for the satellite structure in nTopology 70 
Figure 21. Computational Modeling Operations in Materialise Magics 71 
Figure 22. Arranging build orientationa and nesting in prnting machine control panel 72 
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Figure 23. Metal printing of Cubesat satellite components in Prima Additive 73 
Figure 24. Printed satellite parts of the Cubesat satellite 73 
Figure 25. Optimized U2 satellite structural component 74 
Figure 26. U2 Satellite structure full lattice integration by tetra lattice structure 75 
Figure 27. Facors to enhance structural durability of the hydrofoil 76 
Figure 28. Software integration during the design phase of Hydrofoil 77 
Figure 29. Software integration for design optimization of Aerofoil 78 
Figure 30. Porosity analysis of the aerofoil in Altair Inspire (2024) 79 
Figure 31. Applying BC for the aerofoil flow simulation in Solidworks (2023) 80 
Figure 32. Importing and slicing the aerofoil in the Ultimaker Cura 81 
Figure 33. Printing the aerofoil in Ultimaker (infill pattern and outlayers are shown) 81 
Figure 34. Different Nesting configurations obtained in the Ultimaker Cura platform 82 
Figure 35. Printed full prototypes of the aerofoil 83 
Figure 36. Biomimecking spring mechanism CAD model in Onshape 84 
Figure 37. Multiphysics analysis of the spring mechanism in Altair Inspire 85 
Figure 38. Enhancing Compliant Mechanism design with Additive Manufacturing 86 
Figure 39. Design iteratons comparison conventional vs DfAM 90 
Figure 40. Surface roughness comparison Conventional vs DfAM 91 
Figure 41. Dimentional error comparison Conventional vs DfAM 91 
Figure 42. Technical enhancement synergy 96 
Figure 43. Sustainable goals to achieve in product development 97 
 
 
 
 
Tables 
 
Table 1. AM technologies Performance Matrics 41 
Table 2. FDM surface quality evaluation 42 
Table 3. Mechanical Properties 42 
Table 4. Material cost 42 
Table 5. Quantitative matrics to collect data 43 
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Table 6. Descriptive statistics 43 
Table 7. Components employed for case studies 68 
Table 8. Comparative analysis of Conventional Design vs. DfAM for thesis components
 87 
Table 9. Descriptive statistics for iterations 88 
Table 10. Performance evaluation Conventional Vs DfAM 89 
Table 11. Descriptive Statistics of dimentional error 89 
Table 12. Descriptive statistics of surface roughness 89 
Table 13. Summary statistics Conventional Vs DfAM 92 
 
 
 
Abbreviations  
 
AM Addiitve Manufacturing 
DfAM Design for Additive Manufacturing 
VR Virtual Reality 
2D Two Dimensional 
3D Three Dimensional 
CAE Computer Aided Engineering 
CAD Computer Aided Design 
TO Topology Optimization 
FEA Finite Element Analysis 
CFD Computational Fluid Dynamics 
BCs Boundary Conditions 
FCC Face Centered Cubic 
BCC Body Centered Cubic 
SLS Selective Laser Sintering 
SLA Steriolightography 
FDM Fused  Deposition Modeling 
CMM Cordinate Measuring Machine 
DLP Digital Light Processing 
ERP Enterprise Resource Planning 
STL Standard Triangle Language 
ERP Enterprise Resource Planning 
 
 
 
  
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1 Introduction 
1.1 Motivation for the study 
The adoption of Additive Manufacturing (AM) has grown exponentially in many 
industries, due to its ability to support complex geometries, lightweight structures, and 
rapid prototyping. Design for Additive Manufacturing (DfAM) is an inventive workflow , 
offering engineers to collaborate with advanced design methods over traditional and ign 
methods to generate optimized geometries. The Component development for dynamic 
products, such as hydrofoils and drones, usually involves the designer undergoing 
multiple cycles of redesign, simulation, and prototyping to achieve satisfactory 
performance, and desired functionality. These iterative loops squander precious 
resources and time for product development across industries. 
As a mechanical engineer involved in CAD modeling, structural simulation, and 
manufacturing-inspired design, I have experience in managing the challenges associated 
with design iterations. I am enthusiastic about hydrofoils, drones, satellite and 
compliant mechanisms. These components and systems, where weight, precision, 
hydrodynamics, and aerodynamics functionality are crucial in operation, have drawn me 
to find ways DfAM methods can be improved to yield results faster and better. 
As a student of Industrial Engineering and Management, I am driven by a strong will to 
bridge the gap between technical design optimization and industrial efficiency. My 
studies have acquainted me with a multidisciplinary approach, combining engineering 
design, process improvement, and systems thinking, as a meaningful approach to reduce 
design iterations in additive manufacturing processes. This thesis is an organic extension 
of those aims by examining and comparing AM technologies such as Selective Laser 
Sitering (SLS) and Fused Deposition Modeling (FDM) based on characteristics of 
fabricated components. By quantifying these results using statistical analysis, this study 
hopes to offer practical guidelines for engineers curious to optimize DfAM processes. 
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The prime objective is to reduce number design iteration cycle time while preserving 
design constraints and desired functionality, to enable faster product development in 
high-performance applications in  across industries. 
1.2 Field of Science 
Contemporary development of industries requires integration of multiple scientific 
disciplines to address industrial challenges. This research combines four disparate fields 
of science; Design Engineering and Industrial Design, Manufacturing Engineering, and 
Industrial Management to define a workflow that converts theoretical potential into 
practical industrial solutions. 
Source: Diagram created using 
Napkin AI, based on a descriptive prompt (2025). 
 
Industrial Design and Design Engineering are scientific concepts, encompassing the 
systematic process of designing products to meet design specifications in terms of 
aesthetic, ergonomic, and user experience requirements. The field provides the 
methodological framework for translating user requirements to technical specifications, 
Figure 1. Field of science for thesis development 
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establishing design constraints, and optimizing product dynamics. Customer-centric and 
market orientation of the technical solution are guaranteed by industrial design 
principles, whereas design engineering methodologies establish the analytical level of 
intensity for performance testing and reliability. 
Manufacturing Engineering bridges the gap between production reality and design 
intent, focusing on process optimization, quality control, and setting up manufacturing 
systems. Manufacturing engineering provides technical expertise for converting design 
specifications into producible products, while preserving production efficiency, cost-
effectiveness, and quality assurance. Manufacturing engineering fundamentals focus on 
material choice, process parameter optimization, and production workflow design in a 
way that optimizing the mass production of designs.  
The integration of these four science domains creates a notable gap in current 
manufacturing studies, where technological advancements are typically isolated from 
practical regard for execution. Although each science field has improved significantly, the 
absence of integrated frameworks inhibits the translation of research into industrial 
applications. Ideal technical solutions can be created by design engineering that cannot 
be effectively manufactured by manufacturing engineering, and economic analysis can 
favor solutions that compromise on design integrity or manufacturing quality. 
This cross-functional approach provides industrial relevance by encompassing the whole 
product development process from design concept to manufacturing implementation. 
Modern manufacturing companies require end-to-end solutions reflecting technical 
viability, design superiority, manufacturing efficiency, operational coordination, and 
economic competitiveness simultaneously. By combining design engineering ideology 
with manufacturing engineering practicality, industrial management and economic 
justification, my thesis offers an integrated workflow for engineers to practice overcome 
and barriers and bottlenecks of the existing AM processes.  
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1.3 Additive Manufacturing Workflow 
The thesis research direction mainly integrates Design Engineering, Manufacturing 
Engineering, and Industrial Management. The transition from traditional CAD to 
generative design represents a critical technological advancement for additive 
manufacturing. This computational design transformation directly bonds with the thesis 
objectives of creating industrially viable AM solutions, fundamentally changing how 
engineers approach component design and optimization. 
Generative design differs significantly from traditional CAD (Computer-Aided Design) 
approaches, both methodologically and in output. While the CAD relies on manual input, 
the designer must define the basics of geometries, including design constraints and 
required features. Additionally, handling CAD depends on expertise in the related field 
and industry-related experience in generating the desired geometry. The flip of the side 
generative design leverages algorithms and computational power to automatically 
generate a wide range of optimized design opportunities, and designers can customize 
and generate case-sensitive alternatives. Nevertheless, utilizing a generative design 
approach, the designer must define the design goals, material constraints, 
manufacturing technology, and performance criteria. In addition, the generative design 
software explores numerous approaches to meet desired objectives, where it can 
generate innovative geometries that are often lightweight, lightweight by lattice 
integration,  functionally superior, and tailored for additive manufacturing. Hence, the 
generative design facilitates defining geometries that would be difficult or time-
consuming to achieve manually with traditional CAD tools. 
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The Additive Manufacturing (AM) has become the technological shift for the 
manufacturing of complex components across  fast-evolving industries such as 
automotive, marine and aerospace. AM enabling complex designs that were previously 
impossible to fabricate with conventional manufacturing methods. Despite of these 
advancements, the design process for AM components, particularly in specialized 
applications such as  automobile enegine components, hydrofoils and drones, remains 
inefficient and time-consuming. The Figure 2Error! Reference source not found. 
illustrates design process follow up to generate optimized designs practicing design 
iterations and testing cycles.  
Source: Diagram created using diagrams.net (2025). 
In the aerospace and maritime sectors, where progressive research and development 
involved and the performance is at its best is of such great value, these inefficiencies can 
represent a major obstacle to hindering to innovation. Automotive engine components 
are highly complexity embedded with high precision, drone components and hydrofoils 
require subtle balancing of structural performance. The  weight reduction while 
ensuring the detailed dimension precisness, and aerodynamic or hydrodynamic 
Figure 2. Optimization driven design process of AM practicing design iteration and validation.  
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performance—attributes that are well suited to AM but also represent very complex 
design challenges. 
1.4 Research Onion Framework 
Source: Adapted from Saunders, M., 
Lewis, P., & Thornhill, A. (2007). Research Methods for Business Students (4th ed.). 
Pearson Education, visualized with the assistance of ChatGPT, OpenAI, 2025. 
The Research Onion, shown in Figure 3 shows a structured research design model. Six 
layers of the research are adapted to fit different mixed-methods approaches used to 
evaluate and compare the characteristics and quality related to technical parameters of 
three technologies of Additive Manufacturing, namely SLS, FDM, and SLA. Additionally, 
investigate how does each technology can be used in reducing design iteration cycles 
using fabricated components.  
Figure 3. Adapted Research Onion for the study 
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1.5 Research Philosophy – Pragmatism 
The Research Onion framework supports methodological coherence by a step-by-step 
sequence from philosophical foundations to practical data collection. The pragmatic 
approach fits to the mixed-methods approach, combining quantitative measures 
through CAD modeling, simulation analysis, and testing with qualitative insight through 
design optimization processes and manufacturing workflow observations. This multi-
source triangulation approach embraces both the theoretical underpinnings and the 
practical application requirements, giving balanced coverage of the research objectives 
without sacrificing scientific validity.  
1.6 Research approach – Deductive and Inductive  
This study uses a deductive and inductive research approach to achieve insights into the 
DfAM techniques applied in industrial components from scratch to fully optimal physical 
prototypes. This combination allows the study to be formulated with the advantages of 
two types of research methods as theory-driven and experimental-driven research. 
 
In the deductive phase, research assumptions and hypotheses were derived from a 
comprehensive literature review on a few additive manufacturing technologies, such as 
SLS, FDM, and SLA. Previous studies assisted in understanding design strategies, 
performance factors, and issues related to AM technologies. The theoretical motives 
utilized in deriving detailed assessments on a few technical measures, such as weight 
optimization, strength-to-weight ratio, dimensional accuracy, surface roughness, and 
design iteration efficiency. 
 
This was then followed by the inductive phase, which involved making conclusions from 
the experiential outcomes of the research. CAD models were developed in the VR lab 
and progressively refined using simulation software, and physical components were 
fabricated using different AM techniques. Print quality, material usage, experimental 
observation, and part functionality were tested to identify patterns and relationships not 
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apparent from the literature. These empirical findings helped to narrow the initial 
assumptions, validate theoretical models, and enhance the applicability of the research 
in industrial settings. 
 
By integrating deduction and induction, this study illustrates systematic hypothetical 
analysis with empirical verification to produce a balanced methodology that enhances 
the applicability of the findings. 
1.6.1 Research Strategy  
The research strategy for this study is based on the Research Onion Framework 
(Saunders et al., 2007), ensuring a structured and logical progression from philosophical 
stance to data collection techniques. A mono-method approach was adopted to 
implement the research work focusing on a few case studies, and it’s being single, 
consistent line of analysis on experimental evaluations. This approach supports clarity 
and depth of the research direction in the context of optimizing design for additive 
manufacturing (DfAM) in satellite structural components, hydrofoil, and drone parts. 
The strategy began with selecting case studies from prominent sectors, where specific 
components have been identified as suitable candidates for DfAM redesign. The 
component selection was based on criteria such as geometric complexity, lightweight 
potential, and structural importance. The chosen parts underwent topology 
optimization and lattice structure integration, Flow simulation, FEA and so on, which are 
known as core of DfAM techniques intended to enhance mechanical performance while 
reducing material usage. 
An important design consideration in this strategy was minimization of overhang during 
3D printing, also addressing build orientation, and geometry modifications to improve 
manufacturability. The redesigned parts were then fabricated using two selected 
additive manufacturing technologies: SLS and FDM chosen for their capabilities are 
identical in terms of material specifications, resolution, and cost. 
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As the final phase of the strategy, printed prototypes must be tested and validated. This 
is included FEA to measure and evaluate technical parameters such as strength-to-
weight ratios, dimensional accuracy, surface roughness and a comparison of efficiency 
of printing technology in terms of time, material use. These experimental results have 
been used to conclude about the effectiveness of DfAM techniques, software integration 
and the feasibility of each AM technology. 
This research strategy combines digital modeling, manufacturing, and empirical testing 
in an integrated loop, reinforcing the core goal of the thesis: to reduce design iteration 
cycles and enhance the efficiency of DfAM through an experimental approach.  
1.6.2 Time Horizon – Cross Sectional  
This research was carried out within a cross-sectional time horizon, focusing on a specific 
period. The experiment and analysis implemented for selected components, starting 
with data collection, CAD redesign, simulations, follow up with experimental testing and 
results validation carried out. This time-constrained approach aligns with the practical 
objectives of a master's thesis while allowing for a detailed evaluation of DfAM 
techniques within a defined timeframe. 
1.6.3 Data  
There are two types of data, primary and secondary data were utilized in this study to 
support design, simulation, and analysis tasks. The primary data consisted of CAD models 
of an aerofoil and a hydrofoil that were developed based on profile genratindividually by 
the researcher using generative design and topology optimization techniques. The CAD 
models were developed according to standard criteria (Takuma, n.d., Airfoil Tools, n.d.), 
based on speed, durability, and some of performance and production requirements. 
Additionally following Design for Additive Manufacturing (DfAM) principles. The 
Geometric data, design constraints, FEA, and simulation results of components have 
been utilized for evaluation.  
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To broaden the scope of the thesis, an analysis of a satellite structural component has 
been included as secondary data.  The initial CAD model of the satellite structure has 
been externally provided; subsequently, computational modeling, including TO and 
lattice integration, has been performed. This combination of internally created and third-
party data enabled a diverse and realistic assessment of DfAM methods over a variety of 
functional areas. 
1.6.4 Research gap and justification for the current studies  
Despite significant advancements in generative design methodologies, AM technologies, 
and bio-inspired compliant mechanisms, several critical gaps remain in understanding 
how these can be optimally integrated to reduce design iteration cycles for automotive, 
aerospace, and marine applications where performance-critical designs are highly 
involved. 
i. Limited Integration Studies: While generative design and AM have been 
individually well-studied, research on their integrated application specifically for 
iteration reduction in aerospace and marine industries remains limited in 
exploring. Few studies have been evaluated systematically how different 
generative design platforms facilitate AM technologies to impact overall 
development efficiency (Gibson et al., 2021; Laverne et al., 2017). This 
integration is particularly important given the finding by Hassan and Panchal 
(2021) that software integration capabilities can reduce design-to-manufacturing 
translation time by up to 75%.  
ii. Insufficient Cross-Technology Comparisons: Detailed comparisons on SLS, FDM, 
and SLA that consider the entire design-to-testing workflow have not been 
documented, especially for hydrofoils and drone applications where 
performance is crucial and multifaceted (Thompson et al., 2020). While 
Takahashi et al. (2022) and Mendez and Kumar (2023) provided valuable insights 
for specific components of aerospace and marine applications, broader 
frameworks for technology comparison are lacking.   
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iii. Incomplete Understanding of Process-Specific Design Constraints: While 
general DfAM principles are well-established, technology-specific design 
constraints and their implementation in generative design platforms remain 
inconsistently documented, limiting the effectiveness of automated design 
processes (Yang and Zhao, 2020). This gap is particularly evident in the 
contradictory findings regarding optimal design constraints for SLA-produced 
hydrofoils Zhang and Patel (2023) and Oliveira and Rodriguez (2022).  
iv. Gaps in Multi-Software Workflow Optimization: Research on optimizing 
workflows across multiple software platforms (CAD, simulation, generative 
design, and slicing) is limited, with few studies quantifying the impact of software 
integration on overall iteration efficiency (Wang et al., 2022). This gap is 
particularly relevant given Thompson and Wilson's (2023) finding that platform 
selection can significantly impact design iteration efficiency, to minimizing 
redundant prototyping would be beneficial in terms of time saving and as a cost-
effective approach.  
v. Limited Validation on Virtual Testing Approaches: Although simulation-based 
validation shows promise for iteration reduction, comprehensive validation of its 
effectiveness compared to physical testing, particularly for hydrodynamic 
applications, remains insufficient (Jahan and El-Mounayri, 2021). This gap is 
highlighted by the contrasting findings of Park and Ahmed (2022) and Castillo and 
Bhatt (2023) regarding the effectiveness of digital twins for different application 
domains.  
vi. Insufficient Integration of Bio-inspired Compliant Mechanisms: While bio-
inspired compliant mechanisms have demonstrated significant performance 
advantages (Rodriguez et al., 2023), research on their integration with generative 
design methodologies and AM-specific workflows remains limited. Few studies 
have systematically evaluated how biomimetic principles can be incorporated 
into generative algorithms to reduce design iterations while improving functional 
performance (Wang and Igarashi, 2021). This integration is particularly important 
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given the finding by Matthews and Patel (2023) that biomimetic approaches can 
reduce component count by 60% while improving system-level performance. 
In addition to the experimental analysis using design and printed parts in the VR lab, 
literature and statistical data analysis performed to justify my effort in terms reducing 
design iterations and performance validations. Based on research articles, lab tests, and 
publications data was gathered, analyzed and visualization has been performed under 
the literature review. Additionally, data was build based on printed parts both plastic and 
metal in the VR-laboratory, then arranged accordingly and analyzed to understand the 
behavior of define strategy of the thesis follow up with verifying.   
 
 
 
 
 
 
 
 
 
 
 
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1.7 Research Objectives and Research Questions 
This research aims to contribute to the Design phase of AM. The Design for Additive 
Manufacturing (DfAM) practices for satellite structural components, drone and hydrofoil 
components by addressing the following key objectives: 
1. To develop DfAM design guidelines focusing on topology optimization and lattice 
structure integration, specifically tailored for Fused Deposition Modeling (FDM) 
and Selective Laser Sintering (SLS) processes. 
To compare and evaluate software tools commonly used in DfAM workflows, such as 
CAD, simulation, and generative design platforms, based on their capabilities to support 
efficient and accurate modeling, optimization, and manufacturability for complex 
components. 
2. To identify and apply design validation criteria and rules, ensuring that the 
optimized designs not only meet manufacturing constraints but also maintain 
structural integrity and performance requirements for real-world applications. 
3. To integrate and test the above guidelines by applying them to selected case 
studies (e.g., hydrofoil, aerofoil, and satellite components) through simulation, 
physical prototyping, and performance evaluation. 
By creating a more streamlined design-to-manufacture workflow, this research seeks to 
reduce time-to-market significantly for AM components while improving their 
performance. 
The significance analysis of this research lies in addressing a critical academic and 
industry gap in current DfAM workflows. While much attention has been given to 
improving AM hardware, materials, and other infrastructure less focus has been given 
on optimizing the design process itself. By reducing redundant prototyping within the 
least possible time and creating iteration cycles that are more efficient with seamless 
integration of other parties involved, this framework will enabled.  
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1. Faster design development cycles for performance-critical components with the 
help of generative design tools.  
2. More optimized designs where topology optimized, lightweight while 
maintaining structural integrity, and less post-processing that fully leverage AM 
capabilities 
3. Better integration of biomimetic principles in engineering applications pushes 
boundaries of DfAM creating fully optimized, value added final product.  
The innovative approach combines established design methodologies with nature-
inspired principles to create a realistic framework for engineers working with complex 
AM components to generate fully optimal, ready-to-manuafacture designs. This 
intersection of manufacturing technology and biological design principles represents a 
promising framework in engineering design, especially for applications requiring both 
structural integrity and functional flexibility. 
The current design process for additively manufactured components suffers from 
several key inefficiencies: 
1. Manual redesigns that rely heavily on designer experience rather than 
systematic optimization process powered by generative design software.  
2. Slow validation processes requiring physical prototyping between iterations 
waste time and increase cost. 
3. Fragmented software tools that fail to create a seamless design-to-manufacture 
workflow.  
4. Lack of integration between designing, simulating, manufacturing, and quality 
assuarance 
5. Limited integration of advanced design methodologies like generative design, 
topology optimization and embedding lattice structures.  
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These inefficiencies create the product design development cycles extensive, increased 
costs, and often sub-optimal final designs would cause for customer unsatisfaction. For 
time-sensitive applications in fast growing industries, these delays directly impact 
market competitiveness and slow down the adoption of new technologies. 
The research questions will address the challenges associated with design iterations in 
additive manufacturing, and investigate how digital tools could be used potentially to 
steamline the design process. The prime objective is to identify possible strategies to 
improve speed and quality of design loop and decision making in additively 
manufactured components. Additionally, to investigate the effectiveness of AM 
technologies on design iterations to generate fully-optimal components. On top of that, 
focus on reduce physical prototypes, time, and material saving to make the process 
more sustainable. Research questions are as follows: 
Research Questions 
1. How can design iterations loop to be optimized to improve Design for additive 
manufacturing (DfAM) process? 
2. What are the design specifics for FDM and SLS additive manufacturing 
technologies when integrating lattices and TO processes? 
1.8 Ethical consideration – use of AI tools 
In line with transparency and responsible research practice, this research acknowledges 
the use of artificial intelligence (AI) tools to support in some aspects of the research 
process. AI tools ChatGPT (OpenAI) and Perplexity AI were utilized at the preliminary 
stages of idea generation, namely to refine the methodological design and to form the 
outline of the literature scoping. These tools assisted in formulating relevant research 
paths, understanding theory principles, and researching terminology in Design for 
Additive Manufacturing (DfAM). 
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The Napkin AI was used to generate diagrams by providing customized prompt inputs 
specific to the title and subtitles. Diagrams generated using this tool were read and 
enhanced to align with the subject matter to ensure the consistency with the study 
objectives and representation accuracy. 
Importantly, all the outputs produced with AI support were thoroughly critiqued, 
rewritten, and authenticated to ensure academic integrity. All figures produced using AI 
tools have been attributed in the thesis to ensure ethical transparency. 
This approach complies with sound research practice by recognizing the enabling—but 
cannot operate independently, role of AI in augmenting the research process, without 
sacrificing originality, critical reflection, or analytical nuance of the thesis. 
1.9 Thesis Structure 
The remainder of this thesis is organized as follows: Chapter 2 reviews relevant literature 
on design for additive manufacturing, topology optimization, lattice structures, and 
biomimetic design principles. Chapter 3 details the methodology and framework 
development. Chapter 4 presents design, optimization, simulation, and analysis of case 
studies focusing on hydrofoil, drone, and several other mechanical components. 
Chapter 5 discusses the results and implications, and Chapter 6 concludes with insights 
and recommendations for future research. Chapter 7 concludes my thesis with the 
conclusion and justify my effort on this research work providing answers for research 
questions.  Referrence materials such as research articles, websites, and test papers 
have been listed under the references chapter.  
26 
 
   
 
2 Literature Review  
Generative design is a new approach to engineering design where algorithms are used 
to search huge solution spaces subject to given constraints and objectives (Matejka et 
al., 2018). Recently, pave-the-way development of computational infrastructure has 
widened the array of these approaches to include not only the naive form of topology 
optimization, but also the multi-physics-physics problems and manufacturing 
considerations, which are integrated into the design-phase of design generation itself 
(Wang et al., 2022). This level of integration is especially beneficial for aero and marine 
industry applications because performance prioritization and sizing under severe limits 
of mass and structure are often needed. 
Lee and Markforged (2021) demonstrated that process-aware algorithms have been 
specifically designed to fulfill AM constraints. The ability has been proven by producing 
drone structural components with a 40% weight reduction while preserving the 
mechanical performance. The study highlighted the importance of process-aware 
algorithms, which incorporate process-specific constraints during the generation 
process rather than post-generation as a validation measure. Based on this approach, 
Rodriguez and Altair (2022) establish frameworks for inserting fluid dynamic objectives 
into generative design for hydrofoil applications with a 25% drag reduction compared to 
conventionally designed analogs. This innovation represents the shift from structural 
optimization in isolation to multi-physics generative approaches that serve the complex 
requirements of aerospace and marine components. 
2.1 Integration of simulation tools with AM workflow 
Integration of simulation tools into AM process has been identified as an established 
technique for reducing physical iterations. Kumar and Ansys (2023) set up workflows that 
integrated structural, thermal, and fluid dynamic simulations with AM process 
simulation to accurately predict both the performance of a component and the 
manufacturing outcome. In their aerospace component work, they demonstrated that 
27 
 
   
 
accurately calibrated multi-physics simulations could reduce physical prototyping 
requirements by as much as 60% by simulating ideas virtually. 
For hydrodynamic applications specifically, Zhang et al. (2022) established a simulation 
framework that linked CFD analysis directly with AM process simulation, enabling 
accurate prediction of surface roughness effects on hydrofoil performance. Their 
approach reduced design-to-validation cycles by 45% by identifying printability issues 
and performance impacts before physical production. This demonstrates a critical 
advancement over earlier approaches that treated design simulation and manufacturing 
simulation as separate processes, highlighting the value of integrated simulation 
workflows for components with complex flow-optimized geometries. 
2.1.1 Software platforms could integrate for Design-to-Manufacturing workflows 
The evolution of software platforms has significantly impacted iteration efficiency in 
AM-driven development. Hassan and Panchal (2021) evaluated Autodesk's Fusion 360 
and nTopology for marine applications, revealing that software integration capabilities 
directly influenced iteration cycle time, with fully integrated platforms reducing design-
to-manufacturing translation time by up to 75% compared to disconnected software 
workflows. 
Siemens NX and Altair Inspire represent alternative approaches to integrated workflows, 
with distinct strengths in different application domains. Thompson and Wilson (2023) 
conducted comparative analyses of these platforms for drone component design, 
finding that Altair's emphasis on manufacturing constraints produced more consistently 
printable results, while Siemens offered superior integration with mechanical simulation 
tools. Their work highlighted the importance of platform selection based on specific 
project requirements and team expertise, demonstrating that software platform choice 
can significantly impact design iteration efficiency even before physical production 
begins. 
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2.2 Additive Manufacturing Technologies: Capabilities and Constraints 
for Aerospace and Marine applications  
Additive Manufacturing is a group of technologies that create objects by adding material 
layer by layer based on pre-prepared digital models. Unlike traditional subtractive 
manufacturing, which takes away material from a solid block. AM enables the 
production of complex geometries with reduced material waste and process time. AM 
technologies have been significantly improved, and various processes are created for 
particular applications and materials. A few of the most popular methods are FDM, SLS, 
and SLA. The methods differ in terms of printing mechanisms, material usage, accuracy, 
surface finish, and so on. Additive manufacturing has found numerous applications 
across aerospace, automotive, healthcare, and many consumer products and has 
emerged as a reliable technology today. 
2.2.1 Selective Lase Sintering (SLS): Capabilities and Limitations 
SLS method uses high-power lasers to selectively fuse polymer powder layer by layer, 
offering higher precision for complex geometries and functionally critical parts (Wohlers 
& Gornet, 2016[9]). Recent developments in the SLS technology have been updated 
material choice and process control, with systems today achieving higher accuracy in 
dimension tolerances up to ±0.1mm while offering isotropic mechanical properties 
(Chen and Rodriguez, 2022). This isotropy is a principal advantage over alternative AM 
technologies for parts which has complex geometries and are subjected to multi-
directional loads. 
For aerospace, Martinez et al. (2023) subjected SLS-produced drone components to 
dynamic loading and discovered them to be of structural integrity equal to that of 
injection-molded counterparts but with weight reductions of 30-35% through lattice 
internal structures.  Manufacturing a product without support structures is a significant 
achievement, since it would minimize the post-processing and would be advantageous 
in manufacturing components with complex internal geometries. In marine use, 
Williams and Park (2022) have conducted water tunnel testing of SLS-produced hydrofoil 
29 
 
   
 
sections and found that surface roughness could be optimized to maintain 95% of 
theoretical hydrodynamic performance with optimized process parameters. Their work 
established SLS particularly suitable for functional prototyping of hydrofoils due to its 
mechanical properties and geometric freedom, though finishing the surface remained 
an optimization area. This is a major improvement over earlier SLS implementations, 
where surface roughness significantly degraded hydrodynamic performance. 
2.2.2 Fused Deposition Modeling (FDM): Capabilities and Limitations  
The FDM method is well known as the most accessible AM technology due to its lower 
cost and simplicity in operation (Ngo et al., 2018). The layer-by-layer deposition of 
thermoplastic materials creates inherent anisotropy in mechanical properties, and the 
limitation has been documented properly by Wong and Hernandez (2019). Recent 
developments in advanced materials have expanded FDM's application range, with 
carbon fiber and high-temperature polymers showing specific performance in 
manufacturing functional components. 
For drone application, Wilson et al. (2021) critically evaluated FDM for structural 
components, identifying optimal print parameters for maximum strength-to-weight 
ratios with carbon fiber-reinforced composites. It demonstrated that properly optimized 
FDM components could achieve 70% of the injection-molded equivalents' strength while 
enabling rapid iteration cycles for verification of design. This is a significant enhancement 
from earlier FDM implementations, which typically achieved just 40-50% of injection-
molded strength properties. 
 
For naval applications, FDM suffers from surface finish and water absorption issues. 
Hernandez and Chen (2023) investigated the surface treatments of FDM-processed 
hydrofoil components and found that acetone vapor smoothing followed by hydrophobic 
coating has been able to improve hydrodynamic performance and durability. Their study 
has also highlighted the limitations of FDM in achieving high-performance 
hydrodynamics, particularly water exposure stability in the long term. Further, the 
30 
 
   
 
observation of Williams and Park (2022) concerning SLS suggests that material-specific 
post-processing problems are a requirement for hydrodynamic components. 
2.2.3 Steriolightography (SLA): Capabilities and Limitations 
The SLA is an advanced method in additive manufacturing that has been significantly 
enhanced by digital light processing (DLP) and continuous liquid interface production 
(CLIP) versions (Ligon et al., 2017). The present systems offer good surface finish and 
detail resolution, which is reported by Chohan and Singh (2023) as being as low as 25μm, 
which makes SLA very useful for components requiring high geometric precision. 
As a comparison between manufacturing technologies using aerospace applications, Lee 
and Wang (2022) evaluated SLA for the production of complex drone propeller 
geometries. Finalizing the evaluation, the improved surface finish directly equated to 
improved aerodynamic efficiency compared to FDM counterparts. Their research 
concluded that SLA-produced propellers achieved 95% of the efficiency of conventional 
manufacturing techniques while allowing for the rapid iteration of complex blade 
geometries. This high efficiency was in direct comparison to FDM-produced propellers, 
which achieved only 80-85% efficiency on average due to surface flaws. 
 
For hydrofoil use, SLA offers benefits in surface finish but is challenging in material 
properties, where Zhang and Patel (2023) conducted detailed evaluations of SLA resins 
for hydrofoil prototyping. Summarizing their work, while initial hydrodynamic 
performance was improved due to excellent surface finish, material degradation upon 
exposure to water for long periods limited functional test duration. They highlighted the 
importance of resin selection and post-curing operations for marine applications, 
demonstrating that in some cases, SLA's high surface finish had to be sub-optimized 
against material durability considerations. 
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2.2.4 Strategies for reducing design iterations in Aerospace and Marine Industries. 
In aerospace and marine uses, minimizing design iterations is critical for accelerated 
development cycles, cost savings, and improved time-to-market. Among some of the key 
strategies are adopting Design for Additive Manufacturing (DfAM) principles that enable 
engineers to design components for performance as well as manufacturability from the 
start. Generative design and topology optimization tools are gaining increasing traction 
for driving the adoption of design refinement as a function of performance requirements. 
In addition, simulation-based design and digital twin technology provide real-time 
validation and cycle minimization before actual prototyping. These strategies are 
particularly useful in managing the complexity of high-performance, lightweight 
components used in both industries. 
2.2.5 Digital Twin approaches to Virtual Validations  
The Digital twin strategies, which maintain synchronized digital and physical 
representations of components, have also been demonstrated the possibility to reduce 
the number of iterations in design phase. Park and Ahmed (2022) developed a 
comprehensive digital twin framework for additive manufactured drone components 
that employed real-time test data to refine simulation parameters. Their framework 
reduced physical iterations by 60% through continuous updating of virtual models, 
enabling the high-accuracy prediction of design changes without physical realization. 
For marine applications, Castillo and Bhatt (2023) utilized digital twins for hydrofoil 
design that integrated hydrodynamic simulation, manufacturing process simulation, and 
physical test data. Their platform enabled simultaneous optimization of performance 
and manufacturability and led to a 35% reduction in overall development cycles and a 
15% improvement in the final component's performance compared to conventional 
methods. These improvements are significant for marine applications because of the 
complex interaction between surface auality, material properties, multidirectional stress, 
and hydrodynamic performance. 
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The application of digital twins represents a major enhancement compared to traditional 
simulation-based approaches, as noted by Kumar and Ansys (2023), who described that 
while standard simulations provide beneficial predictive capability, digital twins enable 
the continuous enhancement of prediction accuracy obtained by feedback loops 
between physical testing and virtual models. 
2.2.6 Parametric optimization and Design space exploration 
The Parametric optimization techniques have been particularly promising in reducing 
iteration cycles for performance-critical applications. Nguyen et al. (2022) developed a 
parametric hydrofoil design system integrated with CFD analysis and AM constraints that 
automated the translation of hydrodynamic performance requirements into printable 
geometries. Their process reduced design-to-test cycles from weeks to days by enabling 
the fast exploration of design spaces constrained by both performance requirements and 
manufacturing constraints. 
For aerospace applications, Kim and Johnson (2022) employed a multi-objective 
optimization framework on drone structural components that simultaneously 
considered aerodynamic performance, structural integrity, and AM printability. Their 
approach reduced design iterations by 40% while improving component performance by 
systematically searching the constrained design space. This multi-objective approach is 
a significant advancement over earlier single-objective optimization methods as it 
addresses the complex interplay between performance requirements and fabrication 
constraints characteristic of aerospace applications. 
The success of parametric approaches appears highly dependent on the quality of 
constraint definition, as noted by Rodriguez and Altair (2022), who found that ill-defined 
manufacturing constraints could lead to technically optimal but practically 
unmanufacturable designs, and stressed the importance of manufacturing experience in 
formulating constraints. 
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2.2.7 Process parameter optimization Iteration reduction 
The Process parameter optimization is now a key method for reducing iterations by 
improving first-time success rates. Velasco and Thompson (2023) developed machine 
learning algorithms to predict optimum process parameters for SLS production of thin-
walled drone components, reducing print failures by 55% and post-processing 
requirements by 40%. Their approach utilized historical production data to develop 
correlations between part geometry, material properties, and optimum process settings, 
it is a kind of running an algorithm for design optimization. 
For marine applications for SLA, Oliveira and Rodriguez (2022) introduced a 
comprehensive parameter optimization system specifically for hydrofoil parts, balancing 
surface finish requirements against mechanical performance. Their research 
demonstrated that optimized parameters could reduce post-processing requirements by 
65% and improve part consistency while reducing iteration time significantly. Their 
findings are in direct contradiction to earlier research by Zhang and Patel (2023), who 
had speculated that post-processing requirements for SLA-fabricated hydrofoils were 
largely unavoidable, citing the rapid rate of process optimization technique development. 
The use of machine learning methods for parameter tuning is a significant advance on 
traditional design of experiments (DOE) methods, Chen and Rodriguez (2022) assert, 
having found that ML-based methods were capable of identifying non-obvious sets of 
parameters that traditional DOE methods are unlikely to catch up. 
 
 
2.3 Comparative studies of AM technologies for Aerospace and Marine 
Applications 
In considering AM methods SLS, FDM, and SLA have been chosen for specific applications, 
and certain applications have made good recommendations to choose the correct 
technology based on the requirement. Takahashi et al. (2022) carried out thorough 
analyses of SLS, FDM, and SLA for thin-walled aerodynamic components, not only 
34 
 
   
 
looking at the ultimate performance of the component but also the overall product 
development time, cost, and iteration efficiency. They built quantitative choice systems 
depending on the component specifications and development stage to determine the 
technology that could affect overall development time up to 60%. 
 
For hydrofoils, Mendez and Kumar (2023) performed comparative analyses by the same 
criteria but with a specific framework placed on hydrodynamic performance and 
iteration efficiency. They ensure that although SLA produced parts with better initial 
surface quality, SLS could produce parts with greater overall quality. For the comparison 
of technologies SLA and SLS, several parameters such as the entire development cycle, 
post-processing requirements, and greater stability in test conditions have been 
considered.  
 
Both studies concluded that every technology has its own inherent characteristics, and 
no single technology did well on all the measures, a result that implies optimal 
technology choice consists of a close examination of specific application requirements 
and development priorities. This emphasizes the importance of comparative processes 
that combine technical and practical concerns 
 
2.3.1 Iteration efficiency across development phases 
The relationship between choosing the correct technology and iteration performance 
varies significantly across development phases. Zhang and Morales (2022) tracked 
iteration cycles of drone components from initial preparation and planning to final 
output, and they found that optimal technology selection varied as development 
progressed. Their results have shown FDM to be more efficient in conceptual design 
phases (saving iteration time by 65%), and SLS was optimal in functional test phases 
(reducing iterations by 40%). 
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For hydrofoil development, Castillo et al. (2023) identified matching phase-specific 
strengths, where FDM was best suited to early concept validation and SLA delivered 
strength in detail design phases where hydrodynamic performance was most important. 
Their work indicated potential strengths of multi-technology strategies shifting between 
platforms as development progresses. Phase-specific strategy differs from earlier work 
testing technologies in silos, giving a more nuanced understanding of technology 
selection throughout development. 
Both findings of the aerospace and marine applications show remarkable similarity in 
technology phase-specific advantages, regardless of the diversified performance 
requirements of these applications. This suggests that development-phase concerns can 
be equally important as application-specific needs in selecting AM technologies, a notion 
not accepted in earlier literature. 
2.4 Bio Mimicking Compliant Mechanisms 
The Bio-inspired design has become a powerful paradigm that uses nature's inheritance 
systems and structures as the source of inspiration to address complex engineering 
challenges (Shu et al., 2011). Compliant mechanisms, which achieve motion through 
elastic deflection of compliant members rather than traditional rigid-body joints, 
constitute a leading field of application of biomimetic principles (Howell et al., 2013). 
Compliant biomimetic mechanisms have been found to yield major advantages in 
aerospace and marine applications, since their key features, such as mechanical 
simplicity, weight reduction, and performance efficiency, etc.  
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Nature-inspired biomimetic designs have become increasingly prevalent in industrial 
applications due to their inherent durability and easy adption to different operating 
conditions creating a revolutionary movement in modern engineering. The Deployment 
of biomimetic design is a formal process through a functional abstraction methodology 
that, in a systemic manner. Deconstructs biological systems, maps their function into 
engineering needs, quantifies the dimensional and performance metrics of interest, and 
applies scaling techniques that preserve functional principles while spaning the needs of 
industries. The Figure 4 illustrates innovations can be initiated from pure nature 
structures, and perform an analysis follow up with evaluation to define a solution 
address existing challenges in industries which would offer more natural facet. 
Reprinted from "Innovation 
Inspired by Nature: Applications of Biomimicry in Engineering Design," by T. A. Planet & 
E. Peralta, 2024, University of Sville. Copyright 2024 by the authors. Reproduced with 
permission. 
Martínez-Frutos and Herrero-Pérez (2018) demonstrated that biomimetic compliant 
morphologies based on avian wing shape could lead to up to 30% improved aerodynamic 
Figure 4. Innovation inspiration and abstraction by nature 
37 
 
   
 
performance compared to the traditional rigid control surfaces in the case of drones. 
Their computational approach utilized generative design procedures combined with bio-
inspired motifs for generating adaptive wing morphologies that dynamically changed 
shape depending on flow conditions. Similarly, Chen et al. (2022) designed compliant 
propulsion mechanisms for underwater vehicles inspired by fish caudal fin locomotion 
and showed 25% efficiency improvement in energy compared to conventional propellers 
and significantly reduced mechanical complexity and failure points. 
The union of generative design with biomimetic thinking is a very promising direction of 
research. Wang and Igarashi (2021) termed computational frameworks exploiting 
biological motion patterns as functional goals within generative algorithms to enable 
automated compliant mechanism design mimicking specific natural movement. In 
applying their research about drone components bio-inspired by wing movements of 
birds, they showed that bio-inspiration-based generative methods can produce designs 
that are 45% better in performance-to-weight ratio compared with conventional 
topology optimization. 
2.4.1 Design and actuation of a biomimetic Beetle wing 
The paper "Mimicking unfolding motion of a beetle hind wing" by Muhammad et al. 
(2009) presents experimental research that has focused on creating an artificial hind 
wing that replicates the unfolding motion of the Allomyrina dichotoma beetle. Inspired 
by insect flight principles, particularly the foldable wing mechanism of this large beetle, 
the study aims to provide insight for portable nano/micro air vehicles (MAVs) with 
morphing wings. 
 
Based on the working principle of the beetle's wing, observed, two types of artificial 
foldable wing models have been devised: one based on the out-of-plane mechanism 
described in existing literature, and a second designed to mimic the observed in-plane 
unfolding motion. The in-plane model has been designed to replicate specific parts of 
the real wing, such as veins, membrane, bending zone, marginal joint, and medial bridge. 
38 
 
   
 
Both artificial wing models have successfully demonstrated the ability to unfold using 
shape memory alloy (SMA) wires as artificial muscle actuators. The SMA wires provide 
the necessary force and deformation when heated by electric power. The paper 
illustrates that the artificial in-plane wing model more closely mimics the real beetle 
wing's unfolding mechanism compared to the out-of-plane model. This research 
contributes to the understanding and replication of complex biological mechanisms for 
potential application in biomimetic engineering, particularly for morphing wings in MAVs. 
The Figure 5 shows the aforesaid beetle’s wing developed in a CAD software according 
to the research paper describes and embedding required accessories could develop it 
for advance engineering applications.  
 Source: Muhammad, A., Park, 
H. C., Hwang, D. Y., Byun, D., & Goo, N. S. (2009). Mimicking unfolding motion of a beetle 
hind wing. Chinese Science Bulletin, 54(14), 2416–
2424. https://doi.org/10.1007/s11434-009-0242-z 
From an additive manufacturing perspective, this approach has reshaped and simplified 
the complexity in designing and manufacturing. AM technologies by themselves can 
design and simulate in a minimum number of iterations using this type of biomimicking 
mechanism. The fabrication of intricate biomimetic structures are impossible with 
conventional manufacturing methods, but using AM assist in manufacturing this type of 
functional components while preserving their functionality. Additionally, in the case of 
Figure 5. Biomimetic Beetle wing modeled in Onshape 
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compliant mechanisms in particular, additive manufacturing allows for unprecedented 
liberty for engineers to design meticulous, monolithic parts with integrated flexible joints, 
controllable material properties, and complex inner geometries.  
Additionally, this approach is followed by further strength from a holistic compliant 
mechanism design that provides engineers a parametrized joint topology library for 
different requirements of motion. AM supports material selection methodologies 
depending on fatigue lifetime and elastic response characteristics. The big deformation 
analysis algorithms help for reliable behavior prediction of compliant mechanisms, and 
manufacturing restrictions that refine designs into additive manufacturing potential.   
2.4.2 Additive manufacturing braces for Biomemetic compliant mechanisms 
Additive manufacturing has proved to be an ideal manufacturing technology for 
biomimetic compliant mechanisms because natural structures tend to have complex 
geometries that are impossible to fabricate using conventional manufacturing 
techniques (Li et al., 2022). The layer-by-layer construction of AM enables complex 
compliant mechanisms to be fabricated with precisely controlled material distribution 
and mechanical properties. 
Kumar and Reddy (2023) provided in-depth evaluations of AM technologies for the 
creation of biomimetic compliant mechanisms from insect wing structures. Their 
research demonstrated that SLA was extremely well-suited for the creation of fine-
featured compliant hinges with controlled flexibility, having motion ranges within 5% of 
their biological equivalents. SLS, on the other hand, was determined to be useful for 
larger-scale compliant mechanisms that required isotropic mechanical properties, such 
as adaptive wing structures found in birds. 
For naval applications, Zhang et al. (2023) investigated the production of fish-inspired 
flexible fin structures using a variety of AM technologies. Their study showed that multi-
material PolyJet printing offered particular advantages for emulating the graduated 
stiffness profiles of natural fins, enabling more efficient propulsion systems for 
underwater vehicles. This graduated flexibility, difficult to achieve with traditional 
40 
 
   
 
fabrication techniques, resulted in 35% efficiency improvements in hydrodynamic 
performance compared to stiff fin designs. 
2.4.3 Design to Manufacturing approaches for Biomimetic Mechanisms  
These successful biomimetic compliant mechanisms also require design-to-manufacture 
integration, along with ideas to consider both biological motivation and manufacturing 
constraints simultaneously. Patel and Johnson (2022) created frameworks for translating 
biology principles into manufacturability-constrained designs and demonstrated how 
simultaneous integration of AM constraints would reduce iteration cycles by 50% 
compared to manufacturing validation methods following design. 
Liu et al. (2023) developed machine learning models that identified optimal AM process 
parameters for biomimetic compliant mechanisms based on desired mechanical 
performance. Their approach applied vast data sets of biological motion patterns and 
corresponding mechanical structures to predict optimal process settings for specific 
functional requirements. This data-driven approach reduced design-to-manufacture 
iteration cycles by 40% but improved functional performance by 25% compared to 
traditional parameter selection methods. 
Simulation tool coupling has particularly benefited biomimetic compliant mechanism 
design. Hernandez and Thompson (2022) established multi-physics simulation 
environments optimized for flexible structures, enabling accurate prediction of 
mechanical performance as well as results from manufacturing. Their experiments on 
bird-inspired drone wings demonstrated that accurately tuned simulations were able to 
reduce physical prototyping requirements by 65% by accurately predicting complex 
deformation behavior before physical manufacture. 
 
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2.5 Comparative Study of Additive Manufacturing Methods  
A combination of published research data and experimental results have been used in 
conducting a comparative study of selective laser sintering (SLS), fused deposition 
modeling (FDM), and stereolithography (SLA) technologies. Some of the key 
performance measures were print time, dimensional accuracy, surface roughness, 
mechanical strength, and cost.  
According to search results (Formlab comparison), a key metric has been defined to 
evaluate the performance of each technology as described; print time, surface 
roughness, dimensional accuracy, material costs, and design limitations (e.g. minimum 
wall thickness).  
Table 1. AM technologies Performance Matrics 
Technology Print Speed (Controller) 
Surface 
Roughness 
(Ra) 
Dimensional 
Accuracy 
Cost 
(Material) 
FDM 10h 32m (1 assembly) 25.3 µm ±0.15 mm $50 - $150/kg 
SLA 2h 36m 6.8 µm ±0.02 mm $100 - 200/L 
SLS 3h 52m 12.5 µm ±0.05 mm $100/kg 
 
The Table 1. AM technologies Performance Matrics shows the values with respect to 
each parameter measured in terms of performance of each 3D printing method. For 
instance, it is observed that SLA tends to have the highest dimensional accuracy (±0.02 
mm) and least surface roughness (6.8 µm Ra), while FDM tends to have higher print 
speeds and lower material cost but at the expense of surface roughness (25.3 µm Ra) 
and accuracy (±0.15 mm). SLS provides a compromise with medium accuracy (±0.05 mm), 
surface roughness (12.5 µm Ra), and average material cost. 
2.6 FDM Surface roughness evaluation 
The FDM is considered as allow cost printing method and much economical for low 
dimention accuracy and high tolerance components. The subjected study has been 
conducted with PLA-Aluminum filament material having focused on enhance the surface 
42 
 
   
 
roughness, since layer thickness and raster angle affect surface roughness as illustrated 
in the Table 2. FDM surface quality evaluation    
Table 2. FDM surface quality evaluation 
 
 
 
 
Experimental test data referred to several resources, such as Formlabs (2024), Xometry 
Pro (2023), and scientific research articles. The test data referred to test samples, 
including drone wing fragments and hydrofoils, have been printed using each technology. 
The dimensional accuracy has been measured using calipers or a Coordinate Measuring 
Machine (CMM), and surface roughness has been recorded using specific equipment. 
Mechanical properties as shown in the Table 3. Mechanical Properties, i.e., tensile 
strength, have been tested following standard test methods. The costs as shown in the 
Table 4. Material cost involved were material and machine time, and complied with 
manufacturer recommendations and industry magazines. 
Table 3. Mechanical Properties 
Technology Tensile Strength (MPa) 
FDM 32-55 
SLA 55-85 
SLS 48-58 
 
Table 4. Material cost 
Technology Material Cost 
FDM $50 - $150/kg 
SLA $100 - $200/L 
SLS $100/kg 
Layer Thickness (mm) Raster Angle (°) Surface Roughness (Ra, µm) 
0.1 0 10.2 
0.2 45 15.8 
0.3 90 20.5 
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2.7 Statistical Analysis for AM technologies comparison 
Based on data gathered, a statistical analysis was performed to visualize a comparison 
between three vital technologies in additive manufacturing; SLS, SLA, and FDM. The data 
has been accumulated from research articles based on criteria shown in the Table 5. 
Quantitative matrics to collect data, from journals and lab tests to etc.    
Table 5. Quantitative matrics to collect data 
Metric Definition Tools/Instruments 
1.Dimensional 
Accuracy Deviation from CAD model (mm) Calipers, CMM 
2. Surface Roughness Ra (µm) Alicona InfiniteFocusSL 
3.Tensile Strength Maximum stress before failure (MPa) Universal Testing Machine 
4.Print Time Total time per part (hours) Printer software 
Cost Material + machine time (USD) Manufacturer quotes 
 
The statistical analysis has been performed using python 3.12 in a Windows 11 computer, 
and certain libraries such as Pandas, Numpy, Seaborn and Scipy were used to analyse 
data in the Visulal Stiudio workbench. The code snippets has been inserted in the 
content for the explanation on analysis.  
2.7.1 Analysis in Python  
The analysis is started with importing libraries, followed by load data and derive 
Descriptive statistics, relevant code snippets are in the appendices, and the derived 
statisatical parameters are shown in the Table 6. 
Table 6. Descriptive statistics 
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As the shows, several statistical parameters can be seen with respect to the data 
accumulated.   
2.7.2   Plots generation 
Each technical parameter has been obtained from the data, has been analyzed using 
Python and plots have been generated for Surface roughness, Dimensional error, and 
Tensile strength have been described their variation in Figure 6. Surface roughness of 
AM technologies, Figure 7. Dimensional Error of AM technologies, Figure 8. Tensile 
Strength of AM technologies   
 
 
 
 
 
 
 
 
 
Figure 6. Surface roughness of AM technologies 
45 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7. Dimensional Error of AM technologies 
Figure 8. Tensile Strength of AM technologies 
46 
 
   
 
Each plot elaborates the ditingushness of AM technologies, and no single technology is 
good at every three technical parameters, Selective Laser Sintering can be identified as 
a moderate option over others. Nevertheless, engineers must must stric into design 
consideration, must choose the printing technology wisely fulfilling necessary 
constraints to manufacture parts with right technology.   
2.7.3 Efficiency/ Performance advantages of Biomimetic Compliant Mechanisms  
The Biomimetic compliant mechanisms have demonstrated significant performance 
improvements in diverse uses in aerospace and marine applications. Rodriguez et al. 
(2023) conducted a detailed comparison of conventional and biomimetic drone control 
surfaces and concluded that bio-inspired compliant mechanisms achieved weight 
reductions of 40% while increasing aerodynamic efficiency by 25% through adaptive 
shape changes in flight. These improvements were directly attributed to longer flight 
times and maneuverability during outdoor experiments. 
For application on submarines, Wang and Chen (2022) compared fish-based propulsion 
systems to conventional propeller systems on several performance metrics. Their study 
revealed that biomimetic compliant fin systems performed 30% more propulsive 
efficiency at low speeds, and the noise was significantly reduced. Reduced mechanical 
complexity in the systems also translated to improved reliability prediction, where the 
mean time between failures was calculated to be 300% higher than conventional 
propulsion systems. 
The multi-functional nature of biomimetic compliant mechanisms is especially a useful 
advantage. Matthews and Patel (2023) designed adaptive insect-inspired structures for 
drones where sensing, actuation, and structural functionalities are embedded into one 
compliant element. Their systematic methodology eliminated 60% of elements and 
increased system-level performance through eliminating junctions and minimizing the 
requirements of assembly. This multifunctionality, enabled by both aspects of 
biomimetic design and the capabilities of AM production, is considerably more than any 
achieved through conventional systems based on individual components. 
47 
 
   
 
3 Methodology 
The previous chapter outlined literature pertinent to the understanding of the research 
topic as well as assisting in designing the right questions to be used in the Methodology. 
Within this chapter, the research methodology that was used to reach the thesis goals is 
outlined. This chapter outlines the philosophical assumptions, research strategy, 
methodological design, data collection techniques, and analytical tools used within the 
study. The research methodology is structured using the Research Onion model 
(Saunders et al., 2007), which allows systematic research development from the 
philosophical to technical execution. 
3.1 Methodological Choice  
This research utilizes a mono-methods approach of unique practical implementation of 
work path consists  design, computational modeling with different software integration, 
results evaluation while reducing design iterations. Additionally, provide insight for the 
advancement of DfAM techniques. The methodology focuses on defining a workpath 
that reduces additive manufacturing's design iteration cycles for performance-critical 
components in aerospace, and marine industries adding values with biomimtic 
compliant mechanisms. The methodology includes the practical implementation 
constraints to define solutions that are scientifically sound and industry-viable. 
3.2 Case study/ Use case selection 
The thesis has a practical approach to generate the research output, since the study 
commenced by selecting suitable components/ applications from relevant industries. 
The criteria considered on selecting components were; possibility for weight reduction, 
TO, and performance evaluation.  The hydrofoil and aerofoil were modeled from  the 
scratch, while a satellite structure was selected as a use case with a pre modeled CAD. 
Once the CAD are bases are ready, computational modeling was performed and analysis 
for the evaluation.  
48 
 
   
 
During the process, data was gathered focusing on statistical analysis to deepen the 
research approach with data analysis.  
3.3 Design/ Redesign process 
The design phase involves DfAM techniques using different CAD software, depending on 
infrastructure, designer familiarity, geometry constraints; preferred software can be 
chosen, including Onshape, Solidworks, and NX etc. The TO is employed as a key 
technique to reduce unnecessary mass without compromising the overall strength. At 
the same time, lattice structures were implemented to maximize the strength-to-weight 
ratio while maximizing the material distribution. Functionally graded designs were also 
explored to optimize performance parameters across different areas of the component. 
Minimization techniques of support were also employed to ensure that the designs are 
economical and efficient to print, particularly for FDM and SLS technologies. 
3.3.1 Design for Additive Manufacturing Integration 
The integration of Design for Additive Manufacturing (DfAM) principles was a core 
methodological element in this study. This involved embedding guidelines using 
generative design software tools and applying process-specific requirements related to 
additive processes, such as FDM and SLS, to optimize part performance and 
manufacturability. The designs have been modified using DfAM techniques, such as 
topology optimization, internal lattice structures, and minimal support requirements, to 
ensure better material utilization, lightweighting, and process efficiency. The CAD 
models have been iteratively evaluated against DfAM rules to align with AM-specific 
constraints and capabilities to ensure a seamless transition from digital model to physical 
prototype. 
3.3.2 Software Integration  
The methodology with the integration of DfAM utilizes multiple specialized software 
platforms, each serving distinct functions in the design-to-manufacture workflow: 
49 
 
   
 
• Solidworks/Onshape: Used for basic CAD modeling, assembling and biomimetic 
compliant mechanism development. 
• Altair Inspire: Applied for topology optimization, lattice integration and 
structural analysis.   
• nTopology: Utilized for advanced lattice generation and integration into 
optimized structural forms. 
• Python/ Excell: Utilized for data arrangements, cleaning, and analysis has been 
performed.  
 
3.4 Simulation and Analysis 
This section describes the simulation techniques have been applied to evaluate and 
optimize designs developed with DfAM techniques. Upon completion of design/ 
redesign of the components, advanced simulation tools were used to test performance. 
Finite Element Analysis (FEA) was used to analyze the mechanical response under 
environments of realistic loadings, and Computational Fluid Dynamics (CFD) was utilized 
to calculate thermal resistance where applicable. Printability analysis was even 
performed to guarantee the manufacturability of the designs using selected AM 
technologies. This allowed design problems to be resolved early on and supported 
further optimization of geometries for manufacturing in actual environments.  
The simulation process exercised in this research was focused on two main areas: The 
process simulation and structural optimization using multi-scale and multi-material 
approaches.  
3.4.1 Process Simulation for Additive Manufacturing 
Process simulation has been conducted to assess printability and manufacturability 
before the prototype fabrication. Key simulation metrics included the following criteria: 
50 
 
   
 
Overhang Analysis and Support Estimation: Models have been evaluated for overhang 
regions requiring support structures. The aim was to reduce the use of support material 
without compromising the structural integrity of the component. 
Layer-wise Build Simulation: A detailed simulation of the layer-by-layer fabrication 
process was performed to identify potential deformation or thermal distortion and 
deformation issues. 
Support Structure Optimization: Based on overhang analysis, optimized support layouts 
were proposed to minimize post-processing effort while ensuring print stability. 
These simulations helped refine the models to align with the practical constraints of SLS 
and FDM technologies. 
3.4.2 Multi-Scale and Multi-Material Topology Optimization 
To enhance structural performance, both multi-scale and multi-material design 
strategies were implemented. 
Macro-Scale Topology Optimization: At the macro level, topology optimization was 
applied to define the overall geometry of the component. This step was aimed at 
reducing weight while maintaining stiffness and functionality. 
Micro-Scale Lattice Design: At the micro level, different lattice structures were 
embedded within the optimized geometry. The orientation and density of the lattice 
were tailored to improve local mechanical behavior and reduce material usage. 
Multi-Material Implementation: Variable material properties have been introduced by 
assigning different lattice types or densities in specific regions of the same component 
offering extensive stiffness and flexibility. Additionally, Based on functional requirements, 
to enhance the performance of critical applications such as satellite and drone 
components malti material optimization can be utilized. 
These simulation approaches contributed to refining the design for optimal mechanical 
and manufacturing performance.  
51 
 
   
 
3.5 Literature/ Statistical based data anlysis 
In addition to the experimental analysis using design and printed parts in the VR lab, 
literature and statistical data analysis performed to justify my effort in terms reducing 
design iterations and performance validations of additive manufacturing technologies. 
Based on literatures such as research articles, lab tests, and publications data was 
gathered, analyzed and visualization has been performed under the literature review. 
Additionally, data was build based on printed parts both plastic and metal in the VR-
laboratory, then arranged accordingly and analyzed to understand the behavior of define 
strategy of the thesis follow up with verifying.   
3.6 Evaluation Criteria  
The prime objective of this research to evaluate the effectiveness of DfAM incorporation 
with generative design software. The relevant criteria have been chosen based on their 
relevance to both performance and efficiency in the context of additively designed and 
manufactured components. The components of the evaluation matrics include following 
criteria:  
Weight reduction (%): The weight reduction achieved through invented strategy (the 
percentage decrease in weight) utilizing design optimization, topology optimization 
compared to a baseline or conventionally manufactured equivalents.  
Strength to Weight Ratio: A critical parameter in subjected industries, matric is used to 
asses how does design supports structural loads relative to its mass.  
Print time: Evaluate the printing efficiency of each components, by comparing the 
duration for different AM process (for Plastic-FDM and Metal-LPBF). 
52 
 
   
 
Performance:   Implemnet to Evaluate and understand the mechanical performance of 
DfAM-optimized components (e.g., dimensional accuracy, stiffness, load-bearing 
capacity) against conventionally designed components using simulation and empirical 
testing data.  
Source: Diagram 
created using Napkin AI, based on a descriptive prompt (2025). 
This evaluation matrics assist for a detailed and quantifiable comparison of AM 
technologies, while highlighting the practical advantages of utilizing optimized designs 
in industrial applications. 
3.7 Experimental Methodology 
The experimental methodology employed in this research is to explore and validate the 
applicability of DfAM techniques for subjected components, satellite structure, hydrofoil 
and drone components, and biomimetic spring mechanism. Candidate components 
were strategically chosen due to their critical performance requirements, lightweighting 
possibilities, and potential for structural optimization in relevant industries. The inherent 
loading conditions and their potential to benefit from additive manufacturing justified 
due selection as case studies. 
CAD models have been developed using generative design software tools and then 
further refined through advanced computational modeling with the help of DfAM 
Figure 9. Evaluation matrics defined to evaluate the performance 
53 
 
   
 
techniques. This step aimed at reducing material usage while ensuring mechanical 
strength and desired performance. The optimization phase also integrated lattice 
structures where appropriate and emphasized support minimization to align with DfAM 
best practices. In the additive processes, generative design software tools have been 
utilized in optimizing geometries for additive processes. 
Following the design optimization phase, Finite Element Analysis (FEA) was conducted 
to simulate mechanical behavior under prescribed loading conditions. This simulation 
evaluates physical parameters such as stress distribution, deformation, and structural 
integrity to ensure the technical suitability before physical prototyping. 
Upon completing simulations, components meet the performance threshold, and the 
finalized CAD files are prepared for 3D printing using slicing software. This involved 
exporting models in STL format, orienting them for optimal printing, generating support 
structures, and slicing using appropriate software tools for each technology. Care was 
taken to maintain consistency across different technologies to ensure a valid comparison 
of the outcome.  
3.7.1 Additive manufacturing prototyping 
To investigate the performance and suitability of different AM technologies, prototypes 
have been fabricated using two different 3D printing processes: FDM and SLS. These 
technologies were selected based on their industrial use cases, such as aerospace and 
marine domains. Each part has been manufactured using the CAD model to ensure 
consistency and validity in the comparative evaluation. 
Printing materials with plastic were carried out with FDM using the Ultimaker Cura 
machine, and the material was PLA. This process offered rapid prototyping at a relatively 
low cost, although there are some limitations in resolution and mechanical strength. 
Printing parts with metal, SLS was used in the Prima Additive machine with the material 
aluminum, making it ideal for performance-critical applications. 
54 
 
   
 
To set up the printing, each 3D model underwent a standardized fabrication procedure 
specifying printing parameters such as layer height, infill percentage, and orientation. 
Post-processing was performed according to each technology's specific needs, such as 
support removal and powder removal for SLS. The printed components were then 
evaluated through both visual inspection and physical measurements to measure and 
evaluate relevant technical parameters.  
Experimental validation focused on key performance indicators relevant to DfAM, such 
as weight reduction percentage, print time, strength-to-weight ratio, and qualitative 
comparison of the printed components against their traditionally manufactured 
counterparts. The outcomes of the analysis formed the basis for further statistical 
analysis, helping determine the most effective printing technology for reducing design 
iterations while optimizing performance. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
55 
 
   
 
3.8 Validation Methodology 
The overall validation methodology integrates electronic and physical verification 
approaches with performance metrics of the workflow to deliver good design validation. 
Electronic validation involves simulation at multi-scale levels of the component at part, 
assembly, and system levels, performance metric testing to requirements, virtual testing 
under nominal and extreme conditions, and statistical analysis of performance variability 
predicted. This digital process is augmented with rigorous physical verification 
procedures, including the production of prototypes through industrial additive 
manufacturing equipment, dimensional accuracy check and comparison to computer 
models, mechanical test procedures for static and dynamic performance, and functional 
tests under application-dependent conditions.  
Source: 
Diagram created using Napkin AI, based on a descriptive prompt (2025). 
To quantify the efficiency of these validation processes, the method defines specific 
workflow efficiency metrics, including time-to-first-valid-design measures, iterations 
reduced in number, design quality metrics at equivalent development stages, and 
resource utilization efficiency comparisons. As a package, these validation components 
Figure 10. Integrated validation methodology to validate experimental results 
56 
 
   
 
create an integrated framework for ensuring design integrity while reducing the 
development process.  
This methodology chapter presents an end-to-end approach to reducing design iteration 
cycles in DfAM processes for performance-critical components. By integrating advanced 
design tools, implementing stringent validation procedures, and optimizing the entire 
workflow process from concept to manufacturing, the research work presented in this 
book provides a framework that is advantageous to both the technical and procedural 
aspects of AM component development. The subsequent chapters will outline the 
application of this methodology to various case studies and document the ensuing 
design efficiency and component performance enhancement.  
3.8.1 Digital Validation  
The validation procedure in the digital process is facilitated significantly by the 
integration of Altair Inspire's Compensated Geometry feature. The feature enhances the 
multi-scale simulation validation procedure by adding a manufacturing process 
simulation that predicts part distortion during the manufacturing process. By simulating 
aspects such as thermal gradients, residual stresses, and material shrinkage specific to 
additive manufacturing processes, the software generates compensated geometry that 
pre-empts expected deformations. Execution of the method involves comparing the 
simulated printing results and the original design intention, measuring predicted 
deviations, and automatically generating a modified input geometry that, when 
subjected to the manufacturing process, will produce a part with close proximity to 
design tolerances. This predictive compensation approach is a fundamental innovation 
within the digital validation workflow that effectively bridges the gap between digital 
design and physical manufacturing while reducing the number of physical prototypes 
required for validation. 
 
57 
 
   
 
4 Design, Optimization, Simulation, and Analysis 
The empirical settings of the thesis presented in this chapter, includes design, 
optimzations, simulations, and analysis carried out related to each use cases will be 
described. As practicing generative design software approach to optimize the workpath, 
a general framework is introduced, to reduce design iteration cycles in DfAM. The AM 
workpath includes software integration identical for each step, design optimization 
techniques, and process simulation to create a cohesive approach to DfAM for high-
performance components, as seen in Figure 11. Additive Manufacturing workpathThe 
architecture is built around four key pillars: design optimization, lattice integration, 
simulation validation, and manufacturing preparation. 
Source: Diagram created using Napkin AI, 
based on a descriptive prompt (2025). 
 
4.1 Software Integration 
The steps of the AM workflow integrates multiple specialized software tools to maximize 
the output while maintaining design continuity: 
Figure 11. Additive Manufacturing workpath 
58 
 
   
 
• Design Creation Layer: Solidworks and Onshape serve as the initial design 
creation platforms, establishing base geometry and functional requirements. 
• TO Layer: Altair Inspire, Altair HyperMesh functions as the primary topology 
optimization platform, processing design spaces and load cases. 
• Lattice Generation Layer: Lattice generation is considered as a part pf TO, 
nTopology is specialized in generative design and facilitates in lattice integration 
intiutively, also its dedicated environment for lattice structure generation and 
integration offer more controllability and handling features over lattice patterns. 
• Simulation Layer: Multi-physics analysis measures different parameters stress, 
strain, displacement and so on. In the simulation phase the component under 
goes through different loading conditions, flow patterns and many other 
different constraints in a virtual environment to understand its durability. This is 
one kind of non destructive performance validation method in CAE of a 
component prior to fabricate  simulation tools validate designs before physical 
prototyping. 
• Manufacturing Preparation Layer: In this phase optimize the configurations to 
maximize the production, software applications specialized software (Ultimake 
Cura, Materialise Magics). 3D printing software to optimize the nesting to 
increase the number of components to print at a time, avoid overhang 
constraints, and minimize support structures to reduce post processing. 
• Production Planning Layer: Production simulation would visualize how best can 
configure the manufacturing set up using specialized applications ( such Visual 
Components) to understand the factory layout, orientations of machinery and so 
on. Additionally, can leverage to forecast the production based on exxsoiting 
infrastructure and due changes can be done based on planning  to maximize the 
production rate. ERP system integration is necessary for factory-level production 
planning and make the whole process smooth and negotiage bottlenecks. 
59 
 
   
 
4.2 Design Optimization  
Primary designs has been done based on specific requirement (could be 2D or 3D), but 
prior to its physical prototype, it must be optimized at the design phase. This kind of a 
skill that optimize the design by designers in different CAD platforms and utilizing 
generative design software. The modification/ optimoization must preserve the 
customer specification, on top of that engineers may do changes using different 
techniques which would generate the best output and sustainable product.  
4.3 Topology Optimization 
The topology optimization is primarily used to reduce the weight , but not to 
compromise the overall strength, this technique widely used to optimize geometries. 
Depending on load condition to mimize the mass distribution, eventually would generate 
light weight structures.  The additive manufacturing compliments topology optimization 
and most of topology optimized components are additively manufactured, where 
conventional manufacturing methods are impossible to use. Further topology 
optimization incorporate with multi physics simulation would assist to reduce the 
number of iterations saving time, resources, and minimizing material wastage.  
Source: 
Diagram created using Napkin AI, based on a descriptive prompt (2025). 
Figure 12. Topology Optimization process to adapt in geometry optimization 
60 
 
   
 
4.3.1   Aerofoil Flap Actuator 
The framework application to the aerofoil flap actuator demonstrates: 
1. Integrated compliant hinges replacing traditional bearings 
2. Topology-optimized force transmission elements 
3. Consolidated part count through AM design freedom 
4. Weight reduction through strategic material removal 
The Flight control flap actuators are critical systems that need to deliver exemplary 
performance with durability under strict weight and reliability conditions. Optimization 
of such components has evolved significantly by including advanced computational 
methods and manufacturing technologies. Modern optimization approaches combine 
parametric design exploration with topology optimization strategies to come up with 
high-performance actuator systems that provide optimal strength-to-weight ratios while 
ensuring functional reliability under severe aerospace conditions.  
Figure 13. Flap actuator prior to perform TO 
The topology optimization has revolutionized actuator design by mathematically 
determining ideal material distribution, producing organic shapes that efficiently 
manage load paths with reduced mass. The process identifies and eliminates redundant 
material while reinforcing primary stress locations, resulting in structurally sound parts 
with significantly reduced weight penalty.  
61 
 
   
 
When paired with additive manufacturing capabilities, such optimized designs are 
possible with complex internal geometries and part counts reduced to the point of being 
impossible in conventional manufacturing. The Figure 14 shows a topology optimized 
falp acutor in Altair HyperMesh, significant weight reduction can be observed, yet 
maintaining the structural integrity and overall strength.  
 
4.4 Lattice Structures  
Latticing can be defined as one type of smarter piece of designing, generative design 
software facilitates widely to develop geometries with advanced techniques. Latticing 
technologies that enhance the design for additive manufacturing (DfAM) by enabling the 
creation of complex lattice structures leading to make component lighter in weight, yet 
more sttifer and durable. 
Latticing can be defined as meso-level design elements consisting of repeating unit cell 
elements in 3D space. Together with additive manufacturing perspectives, lattices would 
derive many advantages. They reduce an object’s mass, which means less material is 
used. Cellular structures can also give objects extra strength. In the Altair Inspire work 
bench, Implicit modeling drives lattice creation and geometry modification. Engineers 
can leverage lattice variations such as Surface, Strut, Planar, and Stochastic based on 
industry needs. 
Figure 14. Aerofoil flap actuator – Topology Optimized in Altair HyperMesh 2024 
62 
 
   
 
4.4.1 Lattice Generation Methodology 
Modern generative design software takes the lead in latticing technology with unique 
and advanced features that facilitate engineers to enrich their designs. nTopology 
latticing workflow has been empowered with third-generation latticing technology. It is 
enriched with more intuitive and efficient features way to generate lattice structures by 
splitting the process into three core steps: first one needs to specify the unit cell, then 
define a cell map according to the orientation of the geometry, and finally can control 
lattice parameters, and so on.  
Once the lattice is applied, then lattice characteristics can be changed according to de 
sign parameters. In this case unit cell is Gyroid, and it is optional that the unit cell can be 
utilized as single/ double and regular/ inverted. Lattice is concerned widely in additive 
manufacturing perspectives, where lattice must be printable without overhanging an 
self-supports must be optimized. This optimization can be obtained by changing param 
eters such density, size of unit cell (along the x, y, and z directions), and changing the 
orientation of the unit cell to locate unit cells precisely to minimize overhanging.  
Figure 15. Lattice cell orientation in generative design software platform Altair Inspire 
63 
 
   
 
Additionally, in terms of optimizing a part, an assembly, or any other component, engi 
neers’ prime responsibility is to think and plan well in advance and use all kinds of design 
tools to ensure the printability. manufacturability of the product and to reach the de 
sired quality. The Figure 15 illustrates a component that has been optimized in Altair 
Inspire with lattice integration and changed the orientation (in 40 degrees) to ensure the 
manufacturability of lattices while preserving the additive manufacturing standards. The 
section view option in the Inspire workbench can be used to visualize and understand 
the lattice orientation, and its development along x, y, and z directions when it is printing 
as the Figure 15 shows.  
As the figure illustrates, cross sections can be arranged using the icon that has been 
highlighted with purple circles inside the purple rectangle down below to understand 
the lattice development all over the CAD body. Cross sections can be created across all 
three directions; x, y, z, also moving the respective frame along the way lattice 
development animates with the CAD body. Additionally, at the top of the model browser 
there is an option to change the visualization quality from low level to very high. The 
quality of mesh matters a lot and analyses using slicing software such Ultimaker Cura 
and Materialise Magics. To enhance the quality of mesh, lattice visualization must be 
higher depending on geometry that has been used, lattice creation, and topology 
optimization.   
 
Note: There is no golden rule such, all the time to maintain the visualization at higher;  
during design iteration engineers must decide the best level of visualization depending  
on the case. 
 
 
 
 
64 
 
   
 
4.5 Multi-Objective Optimization 
In the Optimization setup, many parameters must be satisfied while achieving required 
structural performance. Engineers must ensure that any of the parties should not 
compromise to make the output more precise. In additive manufacturing point of view,  
more specialized analysis is necessary to fulfill AM related constraints. The Figure 16 
illustrates a map to optimize the additive manufacturing workflow integrating sub 
processes.  
Source: Diagram 
created using Napkin AI, based on a descriptive prompt (2025). 
Skills in computational modeling are essential to optimize the design for additive 
manufacturing starting from the handling the CAD modeling software to advanced 
computational modeling software and 3D printing software along the way, there are 
many constraints to consider and to be proactive with geometry handling techniques, 
optimize the design, and maximize the manufacturing capacity ensuring sustainable use 
of resources. 
Figure 16. Multi-objective optimization in Additive Manufacturing 
65 
 
   
 
4.6 Components Design and Development 
The components have been devided into few categories based on inherent 
characteristics, and described as follows. 
4.6.1 Structural Components 
The development methodology for structural components ( satellite assembly, hydrofoils, 
drone wing, and biomimetic spring mechanism) follows a systematic approach: 
1. Initial requirements analysis (may be received from a customer or stakeholder) 
and loading conditions.  
2. Preliminary design space definition based on installation constraints, define the 
software requirement for basic CAD modeling and assembling.  
3. Advanced computational modeling. Import the CAD model to generative 
software available for Multi-objective optimization.  
4. Perfom FEA validation and refine the structure based on results and 
requirements have been specified. Topology Optimization or Generative Design 
can b e used for advanced optimization.  
5. Leverage capabilities of generative design software for lattice Integration for 
subjected  structures using  for weight reduction while maintaining performances 
such overall strength, structural integrity and so on.  
6. Employee 3D printing slicing software for further design refinements to meet 
manufacturability constraints specific to the selected AM technology.  
4.6.2 Functional Components 
For functional components (aerofoil flap actuator, automotive parts like connecting rod, 
camshaft) the approach is bit more advance since those are subjected for various 
66 
 
   
 
different loads and stresses. Therefore during the design sopace all kinds of constrains 
must be considered and the methodology includes: 
1. Functional requirement specification (loads, stress, possible displacement limits, 
specific tolerance limits, material specifications, and other subjected 
components) and performance metrics definition.  
2. Kinematic analysis and motion envelope determination to ensure that design 
phase covers all necessary constraints for the proposed component.   
3. Once basic CAD modeling has been completed successfully according to the 
dimention and other geometry specifications, can implement the Topology 
optimization with functional constraints 
4. Generally, materials are specified by customers or stakeholders, and also 
Material selection can be done based on both mechanical and thermal 
requirements.  
5. In the process of additive manufacturing, process-specific design adaptations is 
necessary for further refinements upon due selecting the process to fabricate.  
(special consideration on surface quality, avoid overhang, minimize support 
structures, build orientation optimization, nesting optimization to increase the 
rate of production and time saving) 
6. In the design phase post-processing requirement  must be integrated, while 
performing advanced computational modeling in generative design platforms.  
4.6.3 Biomimetic Compliant Mechanisms   
Biomimetic compliant mechanisms have distinguish characteristics, and their 
applicability requires special concerns. Hence the development phase of biomimetic 
compliant mechanisms must follows a distinct methodology to preserve its inherent 
characteristics.  
67 
 
   
 
1. First need to identify a specific biological system, component or any other 
mechanism to analysis and abstract the functional performances.  
2. Ideology transforming to apply for a suitable engineering application is critical, 
by realizing the specific biological principles  to apply mechanical design 
parameters. 
3. Required basic CAD modeling has to be done in Solidworks and Onshape, such as  
Compliant joints, bodies, connectors and flexure development.   
4. Originally biological structures are subjected to non-linear movements ad  
deformation, since due simulation must be implemented follow up with results. 
5. Biological systems has own damping systems to allows for elastic movements, 
where biomimetic compliant mechanisms also should be able to mimic same 
movements with optimal elastic response. Since assigning material is crucial to 
obtain the desired flexibility.  
6. As biomimetic compliant mechanisms should fulfill additional requirement to 
achieve desired shape, movements and other chatacteristics, design iteration 
must be implemented with seamless integration of all sub process of AM.  
Source: Diagram created using 
Napkin AI, based on a descriptive prompt (2025). 
Figure 17. Biomimetic mechanism developmemt cycle 
68 
 
   
 
Since biomimetic mechanisms are enriched with nature inspiration design iteration loop 
has added value features to exercise during the design iteration to generate fully 
optimized output. Value added insights illustrates in the Figure 17.  
4.7 Case Studies 
In this research, case studies have been selected from the aerospace and marine 
industries, components are a hydrofoil, an aerofoil, and a satellite structure. These types 
of parts were selected based on their suitability for advanced computational modeling 
using generative design software. Each case study is a complete process that involves 
initial design or redesign through advanced CAD software, followed by simulation and 
analysis for performance improvement evaluation. 
The hydrofoil and aerofoil geometries have been developed from scratch and employed 
for topology optimization, lattice structure, flow simulation, and other suitable analyses 
for additive-focused enhancements. For the satellite component, however, they began 
with a pre-existing CAD model that was then further optimized using simulation-driven 
optimizations. The shows the list of candidate components employed in the thesis 
including correspond operations have been performed. 
Table 7. Components employed for case studies 
S/N Component Design Approach 
Computational 
Modeling 
Optimization 
Method 
Printing 
Technology 
1 
Cubesat 
Structure 
(U1) 
Conventional Basic CAD Manual 
SLS 
DfAM Altair Lattice Integration 
2 
Cubesat 
Structure 
(U2) 
DfAM 
Basic CAD TO + Full lattice 
integration N/A Altair + nTopology 
3 Hydrofoil  
Conventional CFD (Basic) Manual 
FDM 
DfAM Altair 
Hydrodynamic 
Opt. 
4 Aerofoil  
Conventional Basic CAD Manual 
FDM 
DfAM Solidworks, Altair Aerodynamic Opt. 
5 Biomimetic Spring  
Conventional Basic CAD Manual 
SLS 
DfAM Altair + ANSYS Bio-inspired Opt. 
69 
 
   
 
Each component is subjected to mechanical simulations using Finite Element Analysis 
(FEA), and, where necessary, thermal and aerodynamic performance were analyzed 
through Computational Fluid Dynamics (CFD). This systematic case study approach not 
only established the effectiveness of DfAM but also highlighted the unique strengths and 
limitations of various additive manufacturing processes (e.g., SLS, FDM).   
4.7.1 Satellite structural component optimization 
Cubesat is a special category of nano, dimention of a cubesat is 10 x 10 x 11 centimeter,  
then the requitemnt is to design a lighter and stiffer structure. In the phase of optimizing 
the structure as shown in the Figure 18, from additive manufacturing point of view 
several requiments can be fulfilled as follows:  
1. Assembly consolidation to minimize the vibration 
2. Generate much stiffer structure to withstand loads 
3. Optimize the material usage depending on the loading comditions 
4. Mass reduction strategies while maintaining stiffness requirements 
Each part of the structure has been separately optimized by embedding lattice as follows.  
 
Figure 18. Cubesat Bus Assembly structure, a pre modelled CAD body 
70 
 
   
 
For the basic CAD modelling and geometry splitting were completed in Onshape, and 
two generative design software (Altair and nTopology) have been utilized for advanced 
computational modeling, lattice integration and geometry optimization.  
In lattice embedding, many software can be leveraged with their distinc features, 
handling the lattice cell is crucial to avoild overhang and minimize support structure. Also, 
the quality of mesh play a vital role in physical prototype to print a much precise 
component which satisfy necessary constraints. The Figure 19 and Figure 20 show the 
lattice embedding in two competitive generative design software, which are versatile in 
many areas of computational modeling paradigm. 
Figure 20. Lattice integration for the satellite structure in nTopology 
 
Figure 19. Lattice integration for the satellite structure in Altair Inspire (2024) 
71 
 
   
 
As figures illustrate each software has their own process in lattice embedding, while 
Altair provides series of platform for each category on computational modeling, and 
nTopology has a unique workbench for all operations.  
 
Once the modifications, top of that topology optimization and lattice integration has 
been completed, the component would export as a STL file for the slicing depending on 
material specifcication. Generally, for plastic prints Ultimaker Cura is the most common 
one, but this satellite component was planned to print in metal, Aluminum was chosen  
due ti its inherent material properties suits for light weight structures. The slicing for 
metal Materialise Magics was utilized, is well versed in slicing for metal printing. Further 
refinements such as visualization quality, check and clean the mesh, removing support 
structures, nesting and final preparation as shown in Figure 21 the perform to make the 
print output closely align with the desired specifications.  
At this point, paramers of the component such as lattice orientation, lattic density, 
inward-outward cell thickness, mesh and export quality etc are assessed. If the engineer 
isn’t satisfy with certain parameters of the component, can exercise the design iteration 
loop, and retify the wherever necessary. This software integration framework assist to 
follow the steps in the iteration and modify design until it reach to desired standard, on 
flip of the side framework supports to reduce physical prototypes redundancy in a 
ssustainable approach. 
 
Figure 21. Computational Modeling Operations in Materialise Magics 
72 
 
   
 
After performing several iterations, can finalize the design with accepted quality 
standard, due completion of advanced computational modeling and components are 
ready. Then real metal  printing commences in the printing machine, and as shown in 
the Figure 22 computer screen of the printing machine will display components are 
printing and other machine parameters are there to control during the print.  
 
 
 
A layer by layer, the machine print components as shown in the Figure 23. Laboratory 
Engineer may deal with the machine parameters as the print is going on such as 
temperature, pressure, gas and powder level must be monitored consistently to ensure 
all the parameters are within the accepted range.  
 
Figure 22. Arranging build orientationa and nesting in prnting machine control panel 
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Generally, parts will be printed on a metal palte called as base plate, Upon completion 
of printing, parts detach from the base plate and must do the post processing to clean 
and obtain the surface finish to make the component visually appealing as shown in the 
Figure 24.  
   
Figure 24. Printed satellite parts of the Cubesat satellite 
Figure 23. Metal printing of Cubesat satellite components in Prima Additive 
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4.7.2 Structural optimization with full lattice integration  
The full lattice integration gives extra refinements for a components, specially in 
structural optimization lattice integration assist in optimizing mass distribution while 
maximizing the structural integrity.  
In terms of lightweighting lattice integration is a wise decision with by using precise tools. 
The following shows a structural components which is after design optimization.  Due 
considering the requirement of embedding lattice few factors to be considered, 
specifically design space, type of lattice cell type,orientation of the lattice cell, suopprot 
strctures and so on. There are various lattice cells can be found in catelogues in 
generative design software, out of them, Gyroid is the most popular lattice cell type, 
other than FCC and BCC are often, additionally there are many to choose depending on 
the requirement.    
The lattice integration starts with a STEP file of a CAD model designed in a CAD software, 
then it must be imported into a generative design software platform for advance 
computational modeling . The Figure 25 shows an extended  CAD model of the Cubesat 
satellite structure, this is two times bigger than the generic Cubesat bus assembly, and 
known as U2.  
Figure 25. Optimized U2 satellite structural component 
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Upon  completion of basic CAD modeling, the TO has been performed in Altair to 
generate more feasible structure while optimizing the mass distribution to maintain 
structural integrity. Then for further optimization with full lattice integration nTopology 
has been chosen since its unique and advanced features that facilitate engineers to 
enrich their designs. nTopology latticing workflow has been em powered with third-
generation latticing technology. It is enriched with more intuitive and efficient features 
way to generate lattice structures by splitting the process into three core steps: first one 
needs to specify the unit cell, then define a cell map according to the orientation of the 
geometry, and finally can control lattice parameters, and so on. This modeling approach 
simplifies the complexity of latticing workflow for engineers to modify designs 
seamlessly. The Figure 26 shows fully lattice embedded structural component in the 
nTopology work bench.  
 
Figure 26. U2 Satellite structure full lattice integration by tetra lattice structure 
The full lattice integration offers various benefits for structures such as best option for 
ligh weighting, porosity free, structural flexibility and so on. Additionally, nTopology 
facilitates with intuitive features in creating full body lattice can be seen in the Figure 
26. U2 Satellite structure full lattice integration as ntop notebook has been developed 
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to generate desired geometry. The pathway is clear and platform itself supports for 
designers to choose the consecutive steps and real time troubleshoots.  
4.7.3 Hydrofoil Optimization 
Hydrofoils are one type of widely used component which require robustness to wistand 
loads and geometry optimization to negotiate various flow patterns in a marine 
environment.  
 
 
 
 
 
 
Source:Diagram 
created using Napkin AI (2025). 
Hydrodynamic performance is the most crucial parameter of a hydrofoil, and during the 
design phase engineers must ensure the design satisfy hydrodynamic aspects, since it 
directly impacts the efficiency and speed of watercraft. Additive manufacturing, 
smulation phase to test the hydrodynamic performance and to optimize the geometry 
untill reach to the required standard. This is kind of an iterative development process, 
generative design software platforms can be used prior to physical prototypes to validate 
the design which leads to reduce the design iterations. AM facilitates to optimize the 
material utilization which would benefit for hydrofoils and aerofoils kind of components 
to prevent damages to its geometry. Also, additive manufacturing allows for advanced 
and precise material distribution to enhance cavitation resistance. 
Figure 27. Facors to enhance structural durability of the hydrofoil 
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Further, to acquire extensive stability and ensure the functionality, internal space 
optimization is crucial; due consideration Additive manufacturing techniques facilitates 
the creation of complex internal structures that can enhance stability and stiffness 
without increasing weight. Moreover, hydrofoils must perform well under various 
operating conditions, including different speeds, loads, wave patterns, and 
environmental conditions. Additive manufacturing allows for the creation of designs that 
can dynamically respond to these conditions through design iterations, where CFD 
simulation can be used for the performance evaliuation under various environmental 
conditions, and FEA can be used for multi physiscs analysis to generate more durable 
design. 
The left side of the above figure shows a primarly designed hydrofoil in a CAD software 
prior to optimize. At the basic design, must get the desired shape of an object. Then it 
would import to a generative design software as shows in the right side. Generative 
design software for advanced computational modeling such as weight reduction, lattice 
integration, and CFD simulation etc.  
For this hydrofoil; vertically oriented Planar lattice has been integrated considering the 
direction loading conditions. Next step is to go for the prototype simulation to give it a 
real environmental conditions and test it in a virtual environment to test for its durability 
to visualize the performance. Based on derived simulation results, further geometry 
optimization can be done in the CAD, then in the generative software platform and  so 
on. This iteration could be minimized depending on the software utilized and skill level 
of the designer.   
Figure 28. Software integration during the design phase of Hydrofoil 
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4.7.4 Drone Wing structural optimization 
Aerofoils are also, similarly or even more crucial components compared to hydrofoils;  
necessarily to be optimized to reach performance level due to its critical oprating 
conditions need to encounter. Additive manufacturing (AM) is one of specific technology 
which offers much possibilities to modify, develop, and optimize based on its geometry 
and utilization. It is a significant advantage in optimizing drone wing components by 
addressing aerodynamic performance.  
Additionally, AM facilitates in generating stiffer and light weight profileto withstand 
asymmetric loading conditions while preserving its performance to maintain flight 
dynamics.  CFD simulation could specifically be used for aerofoils to analyze under 
different loading conditions, winds and various turbulence patterns to ensure that the 
wings can handle uneven loads. This would assist engineers to ensure wing structure is 
more durable and reinforced prior to physical prototype validations. All in all, material 
distribution balancing for flight dynamics is critical for maintaining stability and 
maneuverability. Additive manufacturing facilitates in material usage optimization, 
without compromising the overall strength, yet ensure the optimal weight distribution 
across the component. Leveraging various techniques AM could enhance the overall 
performance of drone components. 
Focusing on further optimizing the design and minimizing the redundant prototypes, 
porosity can be analyzed using generative design software prior to fabrication. This 
porosity analyzing capability addresses head-on one of the primary problems in the 
Figure 29. Software integration for design optimization of Aerofoil 
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optimization of additive manufacturing processes - physical iteration minimization 
required to create specification-compliant parts. With the addition of this digital 
validation process after design completion but before production commitment, the 
number of physical prototypes required was reduced by approximately 60%, with the 
same reduction in development time. 
 
These analysis offered better decision-making regarding part orientation and support 
structure location to minimize porosity in structurally significant regions, thereby 
providing a stronger connection between design intent and manufacturing execution. 
Furthermore, generative design software supports for the analysis of various physical 
patrametrs such porosity, micropososity, temperature distribution, shrinkage, and geo 
modulus etc. On top of that, implement a case sentive analysis in a virtual environment 
would be beneficial to understand the behavior of the component in the actual 
environment.  
Additionally, this feature enhance the scope of the framework suggest in my thesis, 
where to minimize the design iterations and physical protypes follow up with real time 
simulations. The case sensitive analysis set upon based on the ground conditions of the 
particular component supposed to be operated. For an example; an aerofoil operates in 
Figure 30. Porosity analysis of the aerofoil in Altair Inspire (2024) 
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air, a hydrofoil operates in a marine environment, likewise ground conditions vary 
according to the applications and purpose.  
The Figure 31. Applying BC for the aerofoil flow simulation in Solidworks shows that 
applying boundary conditions to run a flow simulation for an aerofoil. Theoretically, by 
this point 75% modifications must be completed, and upon running the simulations 
under various ground conditions engineers may optimize the design for better 
advancements. 
Due applying BCs, can observe how flow patterns move around the profile, and based 
on that engineers may refine the basic design and optimization further to obtain a finest 
version. This approach would assist in visualize real ground scenarios in a virtual 
environment, can perform without having a physical prototypes, additionally mitigate 
the risk of failing final products.  
Once the engineer or designer satisfies with the performance of the component such as 
design constraints, FEA, flow simulation and analysis, ready for to fabricate the first 
physical prototypeusing specified material. The Figure 32 shows how does the aerofoil is 
prepared for the printing, first need to import to the slicing software tool either 
Materialize magics (for metal printing) or Ultimaker Cura (for plastic printing).  
Figure 31. Applying BC for the aerofoil flow simulation in Solidworks (2023) 
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In the software settings of the Ultimaker can define the distinguish parameters for a 
component identically, Infill percentage assist in maximize the material distribution and 
reduce the print time, build orientation to position the component on build platform 
focusing to minimize the overhang and support strucre, impacting its quality, 
stremgth,and print time. Additionally, the print speed to print the component with 
desired surface quality. The figure shows how has it been printing in the Ultimaker 
printing machine according to given specifications.  
Figure 33. Printing the aerofoil in Ultimaker (infill pattern and outlayers are shown) 
Figure 32. Importing and slicing the aerofoil in the Ultimaker Cura 
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Moreover, in the context of a factory setting run on a business plan curious to maximize 
the production output while saving time, electricity, and other infrastructure. The Nest 
orientation of components facilitates to increase the production by strategically 
arranging and positioning multiple 3D model within a single build platform. The nesting 
configuration can be decided by the designer based on either requirement or business 
plan, expected rate of production.  
Additionally, the Ultimaker Cura assist in automating nesting upon specifying the 
number of copmponent needs to print at a time, hence designers may choose the best 
nesting configuration to satisfy the given constraints. The Figure 34 shows the two 
different nestings with multiplied aerofoil 3D models imported and correspond slicing to 
maximize the aerofoil production.  
Upon developing this thesis, an aerofoil only printed lasted for seven hours in the past 
resolution, with 0.2mm nozzle have been employed to print the component. With the 
nozzle of 0.2mm utilized can generate very detailed print quality to manage the 
Figure 34. Different Nesting configurations obtained in the Ultimaker Cura platform 
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tolerance. The fabricated material is Polilactic Acid PLA as the default filament of choice 
for most extrusion based 3D printers, with 20% of infill density and triangles pattern with 
0.8mm shell thickness. Upon successful completion of printing full prototype of the 
aerofoil is shown in the Figure 35. 
 
The digital-physical validation bridge is emblematic of the integrated methodology to 
manufacturing workflow optimization that is at the heart of the method presented in 
this research. Objective is to define the seamless integration of software platforms to 
achieve desired efficiency by reducing design iterations and physical prototypes to 
develop sustainable products across industries.  
Figure 35. Printed full prototypes of the aerofoil 
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4.8 Biomimetic Design Implementation 
Nature-inspired biomimetic designs have become increasingly prevalent in industrial 
applications due to their inherent durability and easy adoption to different operating 
conditions, creating a revolutionary movement in modern engineering. Deployment of 
biomimetic design is a formal process through a functional abstraction methodology that, 
in a systemic manner. Deconstructs biological systems, maps their function into 
engineering needs, quantifies the dimensional and performance metrics of interest, and 
applies scaling techniques that preserve functional principles while spaning the needs of 
industries.  
The Following compliant mechanism shown in the Figure 36 has been designed to mimic 
the movement of a bird’s feather, where each rod suppose to turn 30 degrees relative to 
the previous one. Due to the design configuration has embedded the sprin nature, each 
circular part can rotae individually, this motion could potentially be applied to any kind 
of suitable mechanism to function with extra flexibility.   
Further emphasizing, biomimecking has become more advance as integrating new 
features and trying to give more nature biased facet and configurations. Strat from the 
Figure 36. Biomimecking spring mechanism CAD model in Onshape  
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scratch, develop it as a workflow to develop further and scale it depending on industry 
requirement is trending as never before.  
Designing a biomimicking compliant mechanism takes a different approach rather 
conventional CAD bodies. It requires extra care due to its complicated and abnormal 
configuration and functionalities, and they are well suited to replace joints, fixtures 
where subjected for multidirectional stress and complex movements. Upon applying 
supports and certain constraints, behaviour can be analyzed in generative design 
software platforms. The Figure 37 shows multi physics analysis of the spring mechanism, 
upon due applying support for the bottom surface and torsion for each cylindrical part 
individually to rotate, different mechanical parameters can be analyzed.  
Engineers, then can make decision based on the results and adjust initial design 
parameters and load constraints to optimize the design to generate the desired product. 
This analysis make the process much faster facilitating for multiphysiscs analysis to 
optimize the design and  based on results generated, can practice the design loop until 
generate the desired geometry with expected performance standards.  
 
 
Figure 37. Multiphysics analysis of the spring mechanism in Altair Inspire 
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Source: Diagram created using Napkin AI, based on a descriptive prompt (2025). 
 
All in all, the convergence of generative design software has greatly accelerated the 
process by reducing the number of iterations, which allows engineers to specify 
performance measures and constraints. Algorithms enable to scan thousands of design 
options looking for optimal answers that mimic the efficiency of nature. These 
generative techniques, coupled with the layer-building process of AM, have made 
biomimetic design more reliable and sustainable playing field. This enable engineers to 
leverage the years of evolutionary optimization in today's manufacturing systems and 
design lightweight, high-performance parts with fewer material wastes and assembly 
steps. 
 
Due performing the AM process from the scratch to end (design 3D model to print 
physical prototypes), data have been accumulated to analyse. There are four 
components that are fully engaged in the every part of the AM process, and correspond 
performance parameters were measured at each step during the process as shown in 
the Table 8 .  
Figure 38. Enhancing Compliant Mechanism design with Additive Manufacturing 
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The evaluation would perform bassed on the measured values of each parameter, in the 
next chapter for the better understanding in statistical point of view.   
Table 8. Comparative analysis of Conventional Design vs. DfAM for 
thesis components 
 
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5 Evaluation and Discussion 
This chapter synthesizes the evaluations of different cases referred in previous chapters, 
on top of that findings and approximations derived from the software framework 
integration to reduce the iterations of DfAM workflows. The analysis focuses on 
significant improvements in design efficiency, component performance, and 
manufacturing outcomes, while also addressing qualitative aspects such as designer 
experience and knowledge management. The discussion contextualizes these results 
within current industry practices and highlights the broader impact of the framework on 
additive manufacturing adoption in performance-critical applications. 
5.1 Performance Analysis 
The work path exercised in the thesis demonstrated considerable reduction in design 
iterations across all case studies discussed. At some points, no such requirements to 
iterate the design loop several times, since at the first attempt the component is fully 
optimized, then need to complete subsequent steps to complete the process.  The 
framework has shown its effectiveness in design refinements, final refinement phases, 
where conventional approach require multiple iterations, and several prototypes to 
identify issues with physical testing. Following tables illustrate the descriptive statistics 
on each measured parameters during the AM process such as number of iterations in 
the Table 9, surface roughness in the Table 11. Descriptive statistics of surface roughness, 
and dimensional error in the Table 12 .  
Table 9. Descriptive statistics for iterations 
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Statistical point of view Paired t-tests have been used to compare Conventional and 
DfAM approaches for each components (4 x 3D models) for matrics shown in above 
tables.  For the development of thesis same component have been eployed in both 
conventional and DfAM. The t-tests evaluates effectiveness of the DfAM techniques, 
software used to evaluate parameters. The illustrates the each matrics correspond to t-
statistics and p-value.  
Interpretation:  
The conclusion in the the  has been done based on the statistical criteria, p-value 
p-value< 0.05: statistically significant difference 
p-value>= 0.05: No significant difference  
Table 10. Performance evaluation Conventional Vs DfAM 
Table 11. Descriptive statistics of surface roughness 
Table 12. Descriptive Statistics of dimentional error 
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Upon analyzing results of the process, distinguishness of each method, Conventional vs 
DfAM is obvious, where DfAM generate more high value output over conventional in 
every aspects. Three technical parameters evaluated; number of iterations, surface 
roughness, and dimensional error explicitly explaine the significant improvement of 
components developed by DfAM. Moreover, data visulizationhave been performed and 
generated bar plots to illustrate 3 x technical parameters conventional vs DfAM for each 
components of case studies.   
 
Figure 39. Design iteratons comparison conventional vs DfAM 
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Due considering the performance of each technology, DfAM leads in many aspects over 
conventional technologies. The Table 13 summerizes each matrics reated to the process.  
Figure 40. Surface roughness comparison Conventional vs DfAM 
Figure 41. Dimentional error comparison Conventional vs DfAM 
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Table 13. Summary statistics Conventional Vs DfAM 
Metric 
Conventional 
Design 
(Average) 
DfAM Approach 
(Average) Improvement (%) 
Design Iterations 9.3 3.1 67% Reduction 
Print Time (hrs) 10.4 8.1 22% Reduction 
Post-Processing Time (hrs) 4.6 2.1 54% Reduction 
Surface Roughness (Ra, µm) 27.3 22.4 18% Improvement 
Dimensional Error (mm) ±0.17 ±0.11 35% Improvement 
Material Efficiency (%) 65.4 87 33% Improvement 
Total Development Time (weeks) 13.9 5.4 61% Reduction 
 
This significant reductions in iteration cycles is a positive sign of framework integration 
to improve design efficiency, and even with some critical implications to align with 
timelines, and sustainable resource utilizations. The selected software creates a 
workflow, with  identical workload for each software, while end ensuring seamless 
integration make the framework more smoother to follow.  Further, this improvement 
would tailor to reduce time-to-market where follow the framework potentially influence 
in: 
• Finalizing initial design in CAD modeling 
• Analysis and Optimization while preserving mechanical and structural properties 
• Design refinements 
• Manufacturing preparation 
• Reducing physical prototype redundancy 
• Testing and validation 
• Finalizing and make ready the design to print  
Moreover, the framework shows the cost effectiveness across steps of the iteration loop 
as reducing cost for prototyping materials, testing resources, and time related costs etc. 
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The reduction in total cost for the product development presents a strong business case 
for framework adaption for industries, where development and testing  costs higher in 
product validation.  
5.2 Qualitative analysis 
Industries and technologies are evolving rapidly, yet designers’ experience play a vital 
role across all industries when it comes to product development, a qualified designer 
can contribute to development process precisely with advanced technology. This 
framework encourage designers to improve their skills across multiple disciplines to 
improve their confidence in decision making. Creativity is an added value for a designer 
must improve and eventually help to negotiate bottlenecks in product design 
development. Additionally, the framework proposed, reduce the cognitive load for 
designers in managing design constraints while helping to skill development in AM 
specific design techniques  
5.2.1 Framework effectiveness on aerospace applications 
The framework facilitates over aerospace applications in several aspects as follows: 
• Weight distribution optimization:  Critical in design optimization to make the 
product more robust and enhance ethe fuel efficiency. 
• Multi-physics optimization: Crucial in aerodynamic performance enhancement 
• Time-to-market compression: Potentially contribute to win the competitive 
satellite and drone market. 
• Material utilization:  To enhance the sustainability in expensive aerospace grade 
materials. 
The best example is optimization of satellite structural components, with significant 
weight reduction and time saving can be identified as competitive advantages.  
5.2.2 Framework effectiveness on marine applications 
For naval applications, the framework showed specific benefits: 
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1. Hydrodynamic-Structural design Integration: Crucial in developing 
hydrodynamic performance. 
2. Corrosion Resistance Design: In optimizing material selection and through 
material distribution. 
3. Manufacturing Accessibility: Schedule maintenance timely. 
4. Environmental Exposure Resilience: Improved through multi-physics simulation 
to optimize load distribution. 
In the case of hydrofoil, significant weight reduction obtaibned through TO, yet making  
5.2.3 Biomimetic Applications 
For biomimetic compliant mechanisms, the framework showed unique advantages in 
applying over industries: 
1. Material Property Utilization: Effectiveness of using specific materials to mimic 
the elasticity and flexibility. 
2. Non-linear response prediction: Accurate through advanced simulations to read  
the behavior in critical situations. 
3. Maintenance Requirement Reduction: Elimination of unnecessary maintenance 
comes with conventional systems.  
4. Environmental Resilience: Natively nature adapted, and further enhancement 
have been done through elimination of conventional mechanical joints 
The compliant mechanism case study demonstrated these advantages with natural 
movement witjout help of mechanical joints and improvement in cycle life with 
transformative performance. 
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5.3 Framework Limitations and Challenges 
Despite the potential effectiveness of the framework developed, exhibits several 
technical limitations as follows: 
1. Computational resource requirements: Extensive computational capacity is a 
paramount requirement to perform advanced computational modeling.  
2. Multi-material limitations: Industrial limitions and business plan limit the 
material usage to single-material components. 
3. Size constraints: Sizes have been limited by current AM build volumes. 
4. Simulation Fidelity Gaps: Due to impreciseness of data and techniques used by 
designers cause to have discrepancies between predicted and actual behavior. 
5. Post-processing: A hidden dirty of AM, yet has not been addressed well. 
These limitations must be addressed individually to develop as fully pledge framework 
in product development.   
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6 Future Research Directions  
6.1 Technical Enhancement 
There are few technical advancements to improve the current manufacturing paradigm. 
The subsequent research can enhance the capability to include multi-material 
optimization to facilitate more sophisticated part functionality through integrated 
material properties. The Addition of machine learning would facilitate prediction of 
optimal process settings from historical experience, reducing trial-and-error approaches. 
Improved topology optimization algorithms would facilitate better handling of 
competing design objectives in parallel. Increased integration of post-processing 
requirements would render the entire production process more efficient. Finally, 
simulation accuracy enhancement would strengthen the connection between digital 
predictions and real-world results. All these advancements would transcend existing 
limitations while rendering the framework relevant to more manufacturing scenarios. 
Source: Diagram created using Napkin AI, 
based on a descriptive prompt (2025). 
Figure 42. Technical enhancement synergy 
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6.2 Economic and Sustainability impact 
The eco-friendly production process can bring clear benefits both in the company's costs 
and the environment. The fully optimized part production must be implemented to save 
time, cost, labor hours, and to save raw materials. The reduction of the production 
process by consolidating several parts into one component and enabling production 
close to where items are needed would be easier to transport and reduce the costs. 
Maximize the product life cycle to create products with longer lifetimes and improved 
efficiency, which would minimize replacement needs. Most significantly for business, 
perhaps, the production process uses about half the resources of traditional processes. 
All these innovations yield a genuine win-win situation in which environmental goals are 
hand-in-glove aligned with cost savings, and thus adoption is attractive from several 
different angles. 
Source:Diagram 
created using Napkin AI, based on a descriptive prompt (2025). 
Figure 43. Sustainable goals to achieve in product development 
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6.3 Technical research needs 
Future technical research must address the existing issues related to additive 
manufacturing, workflow integration, design for additive manufacturing etc. These are 
some areas could potentially impactful: 
1. Multi-material DfAM Optimization: Methods for optimizing material 
distribution using case sensitive materials 
2. Process Parameter Integration: Deeper integration of process parameters in 
design optimization and validation  
3. Hybrid Manufacturing Approaches: Combining AM with traditional processes in 
integrated workflows and industrializing addive manufacturing with the 
integration of ERP 
4. In-process monitoring integration: Creation of sub loops, closing the loop 
between monitoring and design modifications. Micromanagement must be 
enhanced for the design optimization 
5. Design for Recycling: Recycling is less addressed, and must be included as a part 
of DfAM and Hybrid manufacturing approaches. 
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7 Conclusion 
The thesis has succeeded in creating and validating an integrated workpath for reducing 
design iteration cycles using DfAM technologies for subjected parts. The combination of 
the generative design tools and a general methodology not only significantly shortens 
time-to-market but also offers increasingly optimised designs. Additionally,it enhances 
cost-effectiveness and facilitates applying biomimetic principles in engineering design 
applications.  
The research questions have been formed to answer key inefficiencies in the additive 
manufacturing process with conventional approaches. Upon developing the thesis 
research questions have been answered through the methodology followed.  
Research Question 1: How can design iterations loop to be optimized to improve the 
Design for Additive Manufacturing (DfAM) process? 
The study illustrates that design iterations have been reduced in DfAM upon adapting 
DfAM techniques such as simulation-driven design, Topology Optimization (TO), and 
integration of lattice structures. By employing digital tools to validate printability and 
mechanical performance as a part of the design work path, the need for physical 
prototyping and costly rework has been reduced. Adapting a hybrid research approach 
allowed for theoretical assumptions to be tested through experimentation. Moreover, 
aligning the design process with specific additive manufacturing constraints, such as 
minimizing support structures and optimizing overhang angles, build orientation, and 
nesting, enabled more productive iteration cycles.  
Results of the experiments show that this approach contributed to reducing lead time 
and improving overall design efficiency in the AM work path, especially for performance-
critical components from aerospace and marine sectors, such as hydrofoils and drone 
parts. 
 
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Research Question 2: What are the design specifics for FDM and SLS additive 
manufacturing technologies when integrating lattices and TO processes? 
The research has discovered special design considerations related to SLS and FDM 
technologies. The FDM is typically applicable for plastic components and critical in 
support structures and build orientation. Designers must be precise when specifying the 
correct lattice orientation and positioning. Additionally, designers must be proactive 
when avoiding support structures while ensuring structural stability and printability. The 
SLS offers better geometric freedom for the applicability of topology-optimized 
structures with embedded lattices due to its inherent characteristics of the powder-bed 
fusion process. However, thermal management and powder removal from intricate 
lattice structures remain design constraints in SLS. Further, the study revealed that 
printing parameters of each technology, such as infill percentage, infill pattern, and wall 
thickness, affect the technical feasibility of components. Specifying support structures 
and lattice integration must be based on mechanical function and loading conditions. 
The ultimate findings of the thesis highlight the importance and guidelines of aligning 
the design choices with the properties and weaknesses of the specific AM technology. 
In concluding the thesis work,one must acknowledge limitations such as computational 
resource demands and restrictions on multi-materials; the findings point to the 
revolutionary potential of an integrated DfAM workflow. AM has been a vast subject; 
some areas yet to be integrated with AM must be addressed in future research, such as 
optimizing the whole process to minimize the cost, reducing the post-processing to save 
material, and industrializing the AM to make the flow smooth and fully optimal. AI 
integration into AM would cause the process to flourish with novel applications over 
traditional technologies. On top of that, additional integration of machine learning 
approaches for process parameter estimation and addressing post-processing 
considerations should be the priority of further research. Moreover, to further mature 
this workflow can promote sustainable and efficient product development using DfAM 
across industries. 
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107 
 
   
 
9 Appendices 
Appendix 1. Python code for Statistical Analysis for AM technologies 
comparison based on literature. 
The following Python code was used to perfom statistical analysis and data visualization 
for the comparative study of additive manufacturing components. The data gathered 
from literature, lab tests, and research articles. The code referenced in the Chapter 2, 
Section 8.  
 
Generate statistical parameters 
import pandas as pd 
import seaborn as sns 
import matplotlib.pyplot as plt 
 
data = { 
    'Technology': ['SLS', 'FDM', 'SLA'], 
    'Print_Time': [120, 90, 60], 
    'Dimensional_Error': [0.05, 0.15, 0.02], 
    'Surface_Roughness': [12.5, 25.3, 6.8], 
    'Tensile Strength': [48, 32, 55], 
    'Cost': [50, 20, 70] 
} 
df = pd.DataFrame(data) 
print(df.describe()) 
 
Data visualization 
# Generate barplot for each parameter 
sns.barplot(x='Technology', y='Surface_Roughness', data=df) 
plt.title("Surface Roughness by Technology") 
plt.ylabel("Surface Roughness (µm)") 
plt.xlabel("Technology") 
plt.show() 
108 
 
   
 
 
sns.barplot(x='Technology', y='Dimensional_Error', data=df) 
plt.title("Dimensional_Error by Technology") 
plt.ylabel("Dimensional_Error (mm)") 
plt.xlabel("Technology") 
plt.show() 
 
sns.barplot(x='Technology', y='Tensile Strength', data=df) 
plt.title("Tensile Strength by Technology") 
plt.ylabel("Tensile Strength (MPa)") 
plt.xlabel("Technology") 
plt.show() 
 
  
109 
 
   
 
Appendix 2. Python code for Statistical Analysis for AM technologies 
comparison based on case studies results. 
The following Python code was used to perfom statistical analysis to generate statistical 
parameters  and data visualization for the comparative study of additive manufacturing 
components. The data gathered from literature, lab tests, and research articles. The code 
referenced in the Chapter 5, Section 1.  
 
Generate statistical parameters 
import pandas as pd 
from scipy.stats import ttest_rel 
 
# Data for Satellite Structure, Hydrofoil Part, Aerofoil Part, 
Biomimetic Spring (without Bird Feather Mechanism) 
data = { 
    'Component': ['Satellite Structure (FDM)', 'Hydrofoil Part 
(FDM)', 'Aerofoil Part (FDM)', 'Biomimetic Spring (LPBF)'], 
    'Iterations_Conventional': [7, 10, 12, 15], 
    'Iterations_DfAM': [3, 3, 4, 5], 
    'SurfaceRoughness_Conventional': [31.0, 32.1, 29.8, 12.5], 
    'SurfaceRoughness_DfAM': [25.5, 25.0, 24.2, 8.2], 
    'DimensionalError_Conventional': [0.22, 0.25, 0.22, 0.05], 
    'DimensionalError_DfAM': [0.14, 0.14, 0.13, 0.03] 
} 
 
df = pd.DataFrame(data) 
 
# Descriptive statistics 
print("Descriptive Statistics for Iterations:") 
print(df[['Iterations_Conventional', 
'Iterations_DfAM']].describe()) 
 
print("\nDescriptive Statistics for Surface Roughness:") 
110 
 
   
 
print(df[['SurfaceRoughness_Conventional', 
'SurfaceRoughness_DfAM']].describe()) 
 
print("\nDescriptive Statistics for Dimensional Error:") 
print(df[['DimensionalError_Conventional', 
'DimensionalError_DfAM']].describe()) 
 
# Paired t-tests for each metric 
t_stat_iter, p_val_iter = ttest_rel(df['Iterations_Conventional'], 
df['Iterations_DfAM']) 
t_stat_sr, p_val_sr = 
ttest_rel(df['SurfaceRoughness_Conventional'], 
df['SurfaceRoughness_DfAM']) 
t_stat_de, p_val_de = 
ttest_rel(df['DimensionalError_Conventional'], 
df['DimensionalError_DfAM']) 
 
print("\nPaired t-test for Iterations:") 
print(f"t-statistic: {t_stat_iter}, p-value: {p_val_iter}") 
 
print("\nPaired t-test for Surface Roughness:") 
print(f"t-statistic: {t_stat_sr}, p-value: {p_val_sr}") 
 
print("\nPaired t-test for Dimensional Error:") 
print(f"t-statistic: {t_stat_de}, p-value: {p_val_de}") 
 
import pandas as pd 
import seaborn as sns 
import matplotlib.pyplot as plt 
 
# Data setup 
data = { 
    'Component': ['Satellite Structure (FDM)', 'Hydrofoil Part 
(FDM)', 'Aerofoil Part (FDM)', 'Biomimetic Spring (LPBF)'], 
    'Iterations_Conventional': [7, 10, 12, 15], 
    'Iterations_DfAM': [3, 3, 4, 5], 
111 
 
   
 
    'SurfaceRoughness_Conventional': [31.0, 32.1, 29.8, 12.5], 
    'SurfaceRoughness_DfAM': [25.5, 25.0, 24.2, 8.2], 
    'DimensionalError_Conventional': [0.22, 0.25, 0.22, 0.05], 
    'DimensionalError_DfAM': [0.14, 0.14, 0.13, 0.03] 
} 
df = pd.DataFrame(data) 
 
# Melt for grouped bar plots 
iterations_df = df.melt(id_vars=['Component'], 
value_vars=['Iterations_Conventional', 'Iterations_DfAM'], 
                        var_name='Approach', 
value_name='Iterations') 
sr_df = df.melt(id_vars=['Component'], 
value_vars=['SurfaceRoughness_Conventional', 
'SurfaceRoughness_DfAM'], 
                var_name='Approach', value_name='Surface 
Roughness (Ra, µm)') 
de_df = df.melt(id_vars=['Component'], 
value_vars=['DimensionalError_Conventional', 
'DimensionalError_DfAM'], 
                var_name='Approach', value_name='Dimensional 
Error (mm)') 
 
Data Visualization: bar plot generation  
# Plot 1: Design Iterations 
plt.figure(figsize=(8, 5)) 
sns.barplot(data=iterations_df, x='Component', y='Iterations', 
hue='Approach') 
plt.title('Design Iterations: Conventional vs. DfAM') 
plt.xticks(rotation=45, ha='right') 
plt.tight_layout() 
plt.show() 
 
# Plot 2: Surface Roughness 
plt.figure(figsize=(8, 5)) 
112 
 
   
 
sns.barplot(data=sr_df, x='Component', y='Surface Roughness (Ra, 
µm)', hue='Approach') 
plt.title('Surface Roughness: Conventional vs. DfAM') 
plt.xticks(rotation=45, ha='right') 
plt.tight_layout() 
plt.show() 
 
# Plot 3: Dimensional Error 
plt.figure(figsize=(8, 5)) 
sns.barplot(data=de_df, x='Component', y='Dimensional Error (mm)', 
hue='Approach') 
plt.title('Dimensional Error: Conventional vs. DfAM') 
plt.xticks(rotation=45, ha='right') 
plt.tight_layout() 
plt.show()