Journal
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
Volume 78, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2022.102390
Keywords
Digital twin; Machining system; Intelligent machining; Adaptability; Transfer learning
Funding
- National Key Research and Development Plan of China [2019YFB1706300]
- Fundamental Research Funds for the Central Uni-versities and Graduate Student Innovation Fund of Donghua University [CUSF-DH-D-2020056]
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Digital twin technology has been explored and applied in the machining process. This paper proposes an adaptive reconstruction method to enhance the adaptability of digital twin machining systems. The feasibility of this method is validated through experiments.
Digital twin technology has been gradually explored and applied in the machining process. A digital twin machining system creates high-fidelity virtual entities of physical entities to observe, analyze, and control the machining process in real-time. However, the current digital twin machining systems lack sufficient adaptability because they are usually customized for specific scenes. Usually, if a decision model is directly reused in a different working condition, the accuracy of the decision model is often poor and difficult to work effectively. Meanwhile, the decision model remodeled from scratch will cause a waste of resources and low modeling efficiency. This paper proposes an adaptive reconstruction method to adjust the decision model in the digital twin machining system to enhance adaptability. The proposed method can ensure the rapid development of the digital twin decision model under new working conditions. Finally, taking the drilling process as an example, this paper establishes the experimental drilling platform and verifies the feasibility of this method in the burr prediction task.
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