Stress relaxation behavior and life prediction of 1Cr18Ni9 spring based on PSO-TCN-Attention

doi:10.1007/s40436-024-00538-5

Advances in Manufacturing ›› 2026, Vol. 14 ›› Issue (3): 774-788.doi: 10.1007/s40436-024-00538-5

Stress relaxation behavior and life prediction of 1Cr18Ni9 spring based on PSO-TCN-Attention

Ting-Ting Zhang¹, Zeng-Gui Gao¹, Zi-Feng Xu¹, Chao-Jia Gao¹, Zhen-Hao Yan¹, Zhong-Sheng Qin¹, Li-Lan Liu^1,2

1. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, People's Republic of China;
2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, People's Republic of China

收稿日期:2024-01-08 修回日期:2024-03-10 接受日期:2024-11-26 出版日期:2026-07-23 发布日期:2025-02-21
通讯作者: Li-Lan Liu,E-mail:lancy@shu.edu.cn E-mail:lancy@shu.edu.cn
基金资助:
We want to thank the Shanghai Industrial Collaborative Innovation Project (Grant No. XTCX-KJ-2022-03) and the National Key Research and Development Plan JMRH Key Project (Grant No. 2022YFF1400303) for their financial support.

Stress relaxation behavior and life prediction of 1Cr18Ni9 spring based on PSO-TCN-Attention

Ting-Ting Zhang¹, Zeng-Gui Gao¹, Zi-Feng Xu¹, Chao-Jia Gao¹, Zhen-Hao Yan¹, Zhong-Sheng Qin¹, Li-Lan Liu^1,2

1. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, People's Republic of China;
2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, People's Republic of China

Received:2024-01-08 Revised:2024-03-10 Accepted:2024-11-26 Online:2026-07-23 Published:2025-02-21
Contact: Li-Lan Liu,E-mail:lancy@shu.edu.cn E-mail:lancy@shu.edu.cn
Supported by:
We want to thank the Shanghai Industrial Collaborative Innovation Project (Grant No. XTCX-KJ-2022-03) and the National Key Research and Development Plan JMRH Key Project (Grant No. 2022YFF1400303) for their financial support.

摘要/Abstract

摘要： The degradation or failure of spring performance due to stress relaxation can significantly impact the normal operation of the entire system. Physical models are often used to fit the degradation law of spring stress relaxation. However, the error in the prediction process has not yet been emphasized. Therefore, an error compensation model based on PSO-TCN-Attention is proposed in this study. PSO is applied to optimize the TCN-Attention hyperparameters for data at different temperatures. The prediction model is optimized to learn the difference between the empirical equations and the true value, and the prediction error is then compensated for in the empirical equations to achieve a more accurate prediction of spring degradation. The results indicate that the PSO-TCN-Attention model predicts optimally at four temperatures compared to the TCN, RNN, and LSTM models as well as the empirical equation and delay function models. Based on this, the spring’s life at a failure threshold of 0.95 is extrapolated, and the life distribution is obtained. Additionally, the relaxation mechanism of the spring is analyzed using EBSD and TEM at different temperatures before and after pressurization. The results indicate that the relaxation mechanism of the spring is mainly due to dislocation and slip. The proposed algorithmic model effectively describes the spring degradation trend, suggesting its potential for predicting spring performance degradation.

The full text can be downloaded at https://doi.org/10.1007/s40436-024-00538-5

关键词: Stress relaxation, Error compensation model, Remaining useful life (RUL), Microscopic characterization

Abstract: The degradation or failure of spring performance due to stress relaxation can significantly impact the normal operation of the entire system. Physical models are often used to fit the degradation law of spring stress relaxation. However, the error in the prediction process has not yet been emphasized. Therefore, an error compensation model based on PSO-TCN-Attention is proposed in this study. PSO is applied to optimize the TCN-Attention hyperparameters for data at different temperatures. The prediction model is optimized to learn the difference between the empirical equations and the true value, and the prediction error is then compensated for in the empirical equations to achieve a more accurate prediction of spring degradation. The results indicate that the PSO-TCN-Attention model predicts optimally at four temperatures compared to the TCN, RNN, and LSTM models as well as the empirical equation and delay function models. Based on this, the spring’s life at a failure threshold of 0.95 is extrapolated, and the life distribution is obtained. Additionally, the relaxation mechanism of the spring is analyzed using EBSD and TEM at different temperatures before and after pressurization. The results indicate that the relaxation mechanism of the spring is mainly due to dislocation and slip. The proposed algorithmic model effectively describes the spring degradation trend, suggesting its potential for predicting spring performance degradation.

The full text can be downloaded at https://doi.org/10.1007/s40436-024-00538-5

Key words: Stress relaxation, Error compensation model, Remaining useful life (RUL), Microscopic characterization

Ting-Ting Zhang, Zeng-Gui Gao, Zi-Feng Xu, Chao-Jia Gao, Zhen-Hao Yan, Zhong-Sheng Qin, Li-Lan Liu. Stress relaxation behavior and life prediction of 1Cr18Ni9 spring based on PSO-TCN-Attention[J]. Advances in Manufacturing, 2026, 14(3): 774-788.

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Stress relaxation behavior and life prediction of 1Cr18Ni9 spring based on PSO-TCN-Attention

Stress relaxation behavior and life prediction of 1Cr18Ni9 spring based on PSO-TCN-Attention

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