Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system

Qiang Liu, Hao Zhang, Jiewu Leng and Xin Chen

International Journal of Production Research, 2019, vol. 57, issue 12, 3903-3919

Abstract: Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology.

Date: 2019
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Citations: View citations in EconPapers (9)

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DOI: 10.1080/00207543.2018.1471243

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