A data-driven approach to RUL prediction of tools

Li, Wei1,5; Zhang, Liang-Chi2,3,4; Wu, Chu-Han5; Wang, Yan5; Cui, Zhen-Xiang6; Niu, Chao6

2023-10-01

10.1007/s40436-023-00464

ADVANCES IN MANUFACTURING   Impact Factor & Quartile

2095-3127

2195-3597

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.

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

SCI

English

Corresponding

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[BA17001] ; Baosteel Australia Research and Development Centre (BAJC) Portfolio[IH140100035] ; ARC Hub for Computational Particle Technology[2020ZDZX2006] ; Chinese Guangdong Specific Discipline Project[ZDSYS20200810171201007]

Engineering ; Materials Science

Engineering, Manufacturing ; Materials Science, Multidisciplinary

WOS:001078567400001

SPRINGER

Web of Science

1.UCL, Dept Mech Engn, London WC1E 7JE, England
2.Southern Univ Sci & Technol, Shenzhen Key Lab Cross Scale Mfg Mech, Shenzhen 518055, Guangdong, Peoples R China
3.Southern Univ Sci & Technol, SUSTech Inst Mfg Innovat, Shenzhen 518055, Guangdong, Peoples R China
4.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Guangdong, Peoples R China
5.Univ New South Wales, Sch Mech & Mfg Engn, Kensington, NSW 2052, Australia
6.Baoshan Iron & Steel Co Ltd, Shanghai 200941, Peoples R China

Li, Wei,Zhang, Liang-Chi,Wu, Chu-Han,et al. A data-driven approach to RUL prediction of tools[J]. ADVANCES IN MANUFACTURING,2023.

Li, Wei,Zhang, Liang-Chi,Wu, Chu-Han,Wang, Yan,Cui, Zhen-Xiang,&Niu, Chao.(2023).A data-driven approach to RUL prediction of tools.ADVANCES IN MANUFACTURING.

Li, Wei,et al."A data-driven approach to RUL prediction of tools".ADVANCES IN MANUFACTURING (2023).