A meta-reinforcement learning method by incorporating simulation and real data for machining deformation control of finishing process

Abstract

Finishing determines the final dimension and geometric accuracy of parts, and the finishing process directly affects the stiffness and residual stress redistribution of the workpiece, so the optimisation of the finishing process plays a very important role in deformation control. At present, existing data-driven methods for deformation control need a large amount of labelled training data, which is always a challenge in the manufacturing area, especially for machining deformation. To address the above issues, this paper presents a meta-reinforcement learning model incorporated by simulation and real data, which is trained in a simulation environment with a piecewise sampling strategy for data collection, and can be updated in a real machining environment through a very small number of real monitoring data. The finishing process optimisation for deformation control can be realised using the proposed approach. Finally, the effectiveness of the proposed method is verified both in simulation environment and actual machining, and better results are obtained compared with other existing methods.

KEYWORDS:

• Machining processes
• artificial intelligence
• simulation and real data
• deformation control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [L], upon reasonable request.

Additional information

Funding

The reported research was funded by the National Natural Science Foundation of China [grant number 51775278] and the National Science Foundation of China for Distinguished Young Scholars [grant number 51925505].

Notes on contributors

Changqing Liu

Changqing Liu received the B.S., M.S., and Ph.D. degrees from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2008, 2011, and 2014, respectively. He has been a Professor of Nanjing University of Aeronautics and Astronautics since 2020. His research interest is data-driven intelligent manufacturing.

Yingguang Li

Yingguang Li received the B.S. and Ph.D. degrees from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 1999 and 2004, respectively. He has been a Professor of Nanjing University of Aeronautics and Astronautics since 2008. His research interests are primarily focused on data-driven intelligent manufacturing and advanced composite curing technology.

Chong Huang

Chong Huang received the B.S. and M.S. degree in mechanical engineering from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2018 and 2021, respectively. His research interest is data-driven intelligent manufacturing.

Yujie Zhao

Yujie Zhao received the B.S. degree in mechanical engineering from the Hefei University of Technology, Hefei, China, in 2020, he is currently pursuing the M.S. degree in the Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interest is data-driven intelligent manufacturing.

Zhiwei Zhao

Zhiwei Zhao received the B.S. degree in mechanical engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2015, where he is currently pursuing the Ph.D. degree. His research interest is data-driven intelligent manufacturing.