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Multiconditional machining process quality prediction using deep transfer learning network

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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.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 51675418).

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Bo-Hao Li & Li-Ping Zhao

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Yi-Yong Yao

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  1. Bo-Hao Li
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  2. Li-Ping Zhao
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Correspondence to Yi-Yong Yao.

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Li, BH., Zhao, LP. & Yao, YY. Multiconditional machining process quality prediction using deep transfer learning network. Adv. Manuf. 11, 329–341 (2023). https://doi.org/10.1007/s40436-022-00415-z

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  • DOI: https://doi.org/10.1007/s40436-022-00415-z

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