In industrial production, effectively predicting the remaining useful life (RUL) of cutting tools can avoid overuse or underuse, which is of great significance for ensuring the processing quality of products and reducing enterprises’ production costs. This paper proposes a new method for RUL prediction of cutting tools based on robust empirical mode decomposition (REMD) and capsule bidirectional long short-term memory (Capsule-BiLSTM) network to improve accuracy. On one hand, new state features are extracted using REMD as the input of the deep learning network. On the other hand, a Capsule-BiLSTM network structure is designed to achieve RUL prediction of cutting tools by connecting the four layers. Finally, the effectiveness of the proposed method is verified by a series of cutting tool life tests. Comparison with some mainstream methods indicates that the proposed method has more advantages in RUL prediction of cutting tools with the average accuracy reaching up to 93.97%.
ChengMJiaoLYanP, et al. Intelligent tool wear monitoring and multi-step prediction based on deep learning model. J Manuf Syst2022; 62: 286–300.
2.
ZhouYXueW. Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol2018; 96: 2509–2523.
3.
LiDLiYLiuC. Gaussian process regression model incorporated with tool wear mechanism. Chin J Aeronaut2022; 35: 393–400.
4.
AnQTaoZXuX, et al. A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Measurement2020; 154: 107461.
5.
LaddadaSSi-ChaibMOBenkedjouhT, et al. Tool wear condition monitoring based on wavelet transform and improved extreme learning machine. Proc IMechE, Part C: J Mechanical Engineering Science2020; 234: 1057–1068.
6.
PálmaiZ. Proposal for a new theoretical model of the cutting tool’s flank wear. Wear2013; 303: 437–445.
7.
ZhangYZhuKDuanX, et al. Tool wear estimation and life prognostics in milling: model extension and generalization. Mech Syst Signal Process2021; 155: 107617.
8.
LinRYuYWangH, et al. Remaining useful life prediction in prognostics using multi-scale sequence and long short-term memory network. J Comput Sci2022; 57: 101508.
9.
LiWLiuT. Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling. Mech Syst Signal Process2019; 131: 689–702.
10.
YangYGuoYHuangZ, et al. Research on the milling tool wear and life prediction by establishing an integrated predictive model. Measurement2019; 145: 178–189.
11.
DrouilletCKarandikarJNathC, et al. Tool life predictions in milling using spindle power with the neural network technique. J Manuf Process2016; 22: 161–168.
12.
WangBLeiYLiN, et al. Multiscale convolutional attention network for predicting remaining useful life of machinery. IEEE Trans Ind Electron2021; 68: 7496–7504.
13.
XuXLiXMingW, et al. A novel multi-scale CNN and attention mechanism method with multi-sensor signal for remaining useful life prediction. Comput Ind Eng2022; 169: 108204.
14.
GuoLYuYGaoH, et al. Online remaining useful life prediction of milling cutters based on multisource data and feature learning. IEEE Trans Ind Inform2022; 18: 5199–5208.
15.
YuWKimIYMechefskeC. Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mech Syst Signal Process2019; 129: 764–780.
16.
YaoJLuBZhangJ. Tool remaining useful life prediction using deep transfer reinforcement learning based on long short-term memory networks. Int J Adv Manuf Technol2022; 118: 1077–1086.
17.
ZhouJTZhaoXGaoJ. Tool remaining useful life prediction method based on LSTM under variable working conditions. Int J Adv Manuf Technol2019; 104: 4715–4726.
18.
WangYZhaoYAddepalliS. Practical options for adopting recurrent neural network and its variants on remaining useful life prediction. Chin J Mech Eng2021; 34: 69.
19.
LiHWangWLiZ, et al. A novel approach for predicting tool remaining useful life using limited data. Mech Syst Signal Process2020; 143: 106832.
20.
LiuCZhuL. A two-stage approach for predicting the remaining useful life of tools using bidirectional long short-term memory. Measurement2020; 164: 108029.
21.
KongWLiH. Combining adaptive time-series feature window and stacked bidirectional LSTM for predicting tool remaining useful life without failure data. Int J Adv Manuf Technol2022; 121: 7509–7526.
22.
ZhangXLuXLiW, et al. Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM. Int J Adv Manuf Technol2021; 112: 2277–2299.
23.
BaziRBenkedjouhTHabboucheH, et al. A hybrid CNN-bilstm approach-based variational mode decomposition for tool wear monitoring. Int J Adv Manuf Technol2022; 119: 3803–3817.
24.
ZhuKWongYSHongGS. Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf2009; 49: 537–553.
25.
HuangNEShenZLongSR, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Math Phys Eng Sci A Math Phys Eng Sci1998; 454: 903–995.
26.
LeiYLinJHeZ, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process2013; 35: 108–126.
27.
LiuZZuoMJJinY, et al. Improved local mean decomposition for modulation information mining and its application to machinery fault diagnosis. J Sound Vib2017; 397: 266–281.
28.
LiuZPengDZuoMJ, et al. Improved Hilbert–Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings. ISA Trans2022; 125: 426–444.
29.
WangYWuJChengY, et al. Memory-enhanced hybrid deep learning networks for remaining useful life prognostics of mechanical equipment. Measurement2022; 187: 110354.
30.
SabourSFrosstNHintonGE. Dynamic routing between capsules. In: 31st Conference on neural information processing systems (NIPS 2017), Long Beach, CA, 2017, pp. 3856–3866. Cambridge, MA, USA: MIT Press.
31.
ZhaoCHuangXLiY, et al. A novel remaining useful life prediction method based on gated attention mechanism capsule neural network. Measurement2022; 189: 110637.
32.
ZhaoCHuangXLiY, et al. A novel Cap-LSTM model for remaining useful life prediction. IEEE Sens J2021; 21: 23498–23509.
33.
QinYYuenCShaoY, et al. Slow-varying dynamics assisted temporal capsule network for machinery remaining useful life estimation. IEEE Trans Cybern2022. 1–15. DOI: 10.1109 / TCYB.2022.3164683.
LiuXLiuSLiX, et al. Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network. J Manuf Syst2021; 60: 608–619.
36.
LiYHuangXZhaoC, et al. Stochastic fractal search-optimized multi-support vector regression for remaining useful life prediction of bearings. J Braz Soc Mech Sci2021; 43: 414.
37.
ZouFZhangHSangS, et al. An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis. Measurement2021; 186: 110236.
38.
YuJLiangSTangD, et al. A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int J Adv Manuf Technol2017; 91: 201–211.
