Multiconditional machining process quality prediction using deep transfer learning network

doi:10.1007/s40436-022-00415-z

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (2): 329-341.doi: 10.1007/s40436-022-00415-z

• ARTICLES • 上一篇

Multiconditional machining process quality prediction using deep transfer learning network

Bo-Hao Li¹, Li-Ping Zhao¹, Yi-Yong Yao²

1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China;
2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China

收稿日期:2021-08-14 修回日期:2021-12-06 发布日期:2023-05-20
通讯作者: Yi-Yong Yao,E-mail:yyyao@mail.xjtu.edu.cn E-mail:yyyao@mail.xjtu.edu.cn
作者简介:Bo?Hao Li received the B.S. degree in Mechanical engineering and automation from School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, in 2016. Currently, he is pursuing the Ph.D. degree at the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an. His research interests include manufacturing system modeling and machining process quality prediction.
Li?Ping Zhao received the Ph.D. degree in mechanical manufacture and automation from the School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China, in 1996. Currently, she is a Professor with the School of Mechanical Engineering, Xi'an Jiaotong University. Her research interests include quality control engineering, manufacturing system modeling, and integrating computer aided design/computer aided process planning/computer aided engineering (CAD/CAPP/CAE).
Yi?Yong Yao received the Ph.D. degree in mechatronic engineering from the School of Mechanical Engineering, Xi'an Jiaotong University, Shaanxi, China, in 2002. He is currently an Associate Professor with the School of Mechanical Engineering, Xi'an Jiaotong University, Shaanxi. His research interests include intelligent control technology, mechatronics, and product security technology.
基金资助:
This study was supported by the National Natural Science Foundation of China (Grant No. 51675418).

Multiconditional machining process quality prediction using deep transfer learning network

Bo-Hao Li¹, Li-Ping Zhao¹, Yi-Yong Yao²

1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China;
2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China

Received:2021-08-14 Revised:2021-12-06 Published:2023-05-20
Contact: Yi-Yong Yao,E-mail:yyyao@mail.xjtu.edu.cn E-mail:yyyao@mail.xjtu.edu.cn
Supported by:
This study was supported by the National Natural Science Foundation of China (Grant No. 51675418).

摘要/Abstract

摘要： The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00415-z

关键词: Multiconditional machining process, Intelligent manufacturing, Deep transfer learning, Quality prediction, Process stability

Abstract: The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00415-z

Key words: Multiconditional machining process, Intelligent manufacturing, Deep transfer learning, Quality prediction, Process stability

Bo-Hao Li, Li-Ping Zhao, Yi-Yong Yao. Multiconditional machining process quality prediction using deep transfer learning network[J]. Advances in Manufacturing, 2023, 11(2): 329-341.

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Multiconditional machining process quality prediction using deep transfer learning network

Multiconditional machining process quality prediction using deep transfer learning network

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