A cloud service platform integrating additive and subtractive manufacturing with high resource efficiency

Qian, Cheng; Zhang, Yingfeng; Liu, Yang; Wang, Zhe (2019-12-20)
 
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URI
https://doi.org/10.1016/j.jclepro.2019.118379

Qian, Cheng
Zhang, Yingfeng
Liu, Yang
Wang, Zhe
Elsevier
20.12.2019
doi:10.1016/j.jclepro.2019.118379
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022031723983

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vertaisarvioitu
©2019 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/
This research was supported in part by the National Natural Science Foundation of China (51675441), in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University (Z2017014), and in part by the 111 Project Grant of NPU (B13044). The authors would like to thank the editors and reviewers for their dedication to this research. We acknowledge the opportunities of on-site surveys brought by the Department of Mechanical and Energy Engineering, Southern University of Science and Technology (China). We appreciate the valuable suggestions to this research from Dr. Ying Liu and Prof. Ou Tang.
Tiivistelmä
Cloud manufacturing has been studied for years, yet commercial implementations are still limited. The recent advances in information technology have stimulated the free sharing of additive and subtractive manufacturing (A/SM) resources through cloud services. Currently, due to the lack of a general method to model manufacturing capabilities, as well as the absence of an open platform to integrate business and manufacturing processes, it is difficult to integrate A/SM resources within one platform efficiently and seamlessly. In this research, a service encapsulation model for A/SM resources was described using ontology modeling technique. A collaborative cloud platform integrating A/SM was designed that can provide optimal production plans considering time, cost, quality, and energy waste during manufacturing. The proposed platform and models were demonstrated by a prototype system and tested in a case study, which showed the integrated platform can increase the utilization rate of resources while reducing energy consumption. This research has provided a practical tool for virtualization, integration, and configuration of A/SM resource with high efficiency.
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