A data-driven approach to RUL prediction of tools

doi:10.1007/s40436-023-00464-y

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1): 6-18.doi: 10.1007/s40436-023-00464-y

A data-driven approach to RUL prediction of tools

Wei Li^1,5, Liang-Chi Zhang^2,3,4, Chu-Han Wu⁵, Yan Wang⁵, Zhen-Xiang Cui⁶, Chao Niu⁶

1. Department of Mechanical Engineering, University College London, London, WC1E 7JE, UK;
2. Shenzhen Key Laboratory of Cross-Scale Manufacturing Mechanics, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, People's Republic of China;
3. SUSTech Institute for Manufacturing Innovation, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, People's Republic of China;
4. Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, People's Republic of China;
5. School of Mechanical and Manufacturing Engineering, The University of New South Wales, Kensington, NSW, 2052, Australia;
6. Baoshan Iron & Steel Co., Ltd., Shanghai, 200941, People's Republic of China

Received:2023-03-23 Revised:2023-05-17 Published:2024-03-14
Contact: Liang-Chi Zhang,E-mail:zhanglc@sustech.edu.cn E-mail:zhanglc@sustech.edu.cn
Supported by:
This research was supported by the Baosteel Australia Research and Development Centre (BAJC) Portfolio (Grant No. BA17001), the ARC Hub for Computational Particle Technology (Grant No. IH140100035), the Chinese Guangdong Specific Discipline Project (Grant No. 2020ZDZX2006), and the Shenzhen Key Laboratory Project of Cross-scale Manufacturing Mechanics (Grant No. ZDSYS20200810171201007).

Abstract

Abstract: An effective and reliable prediction of the remaining useful life (RUL) of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance, avoid machine shutdowns and increase system stability. This study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding conditions. The data-driven approach took advantage of bidirectional long short-term memory (BLSTM) and convolutional neural networks (CNN). A pre-trained lightweight CNN-based network, WearNet, was re-trained to classify the wear states of workpiece surfaces with a high accuracy, then the classification results were passed into a BLSTM-based regression model as inputs for RUL estimation. The experimental results demonstrated that this approach was able to predict the RUL values with a small error (below 5%) and a low root mean square error (RMSE) (around 1.5), which was more superior and robust than the other state-of-the-art methods.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00464-y

Key words: Remaining useful life (RUL), Bidirectional long short-term memory (BLSTM), Data-driven approach, Metal forming

Wei Li, Liang-Chi Zhang, Chu-Han Wu, Yan Wang, Zhen-Xiang Cui, Chao Niu. A data-driven approach to RUL prediction of tools[J]. Advances in Manufacturing, 2024, 12(1): 6-18.

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A data-driven approach to RUL prediction of tools

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