Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model
By Jiewu Leng, Qiang Liu, S. Ye, Jianbo Jing, Yan Wang, Chaoyang Zhang, Ding Zhang, Xin Chen. Robotics Comput. Integr. Manuf., 2020
307 citations recorded by the source corpus.
Abstract Increasing individualization demands in products call for high flexibility in the manufacturing systems to adapt changes. This paper proposes a novel digital twin-driven approach for rapid reconfiguration of automated manufacturing systems. The digital twin comprises two parts, the semi-physical simulation that maps data of the system and provides input data to the second part, which is optimization. The results of the optimization part are fed back to the semi-physical simulation for verification. Open-architecture machine tool (OAMT) is defined and developed as a new class of machine tools comprising a fixed standard platform and various individualized modules that can be added and rapidly swapped. Engineers can flexibly reconfigure the manufacturing system for catering to process planning by integrating personalized modules into its OAMTs. Key enabling techniques, including how to twin cyber and physical system and how to quickly bi-level program the production capacity and functionality of manufacturing systems to adapt rapid changes of products, are detailed. A physical implementation is conducted to verify the effectiveness of the proposed approach to achieving improved system performance while minimizing the overheads of the reconfiguration process by automating and rapidly optimizing it.
Subfields: Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/bc7c24679103252c66b5e4df22159dda91a5fd5e
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In Industrial and Manufacturing Engineering
Citations in the corpus
- A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “2021), drive the movement of digital models on the virtual shop floor and thereby optimize manufacturing system configurations or shop scheduling (Leng et al. 2020; Liu et al. 2021a; Fang et al. 2019; Guo et al. 2021; Zhang et al. 2020a, 2021b).”