Deep multi-task network based on sparse feature learning for tool wear prediction

Abstract

Tool wear monitoring plays a key role in the machining industry to increase productivity and reduce downtime. The Data-driven monitoring techniques have been successfully applied in the tool wear prediction in recent years. However, the terrible environment and the varying machining parameters make the data quality and the distribution complex, which limits the performance of data-driven prediction models. How to build a model for different data distribution under different working conditions adaptively is an important research topic. To solve this problem, this paper proposed a new deep multi-task network based on sparse feature learning for tool wear prediction. By introducing the L21 norm and F-norm regularization term to the network loss function, the model can capture the tool wear characteristic sparsely from the input frequency spectrum. Experiment results on a machine tool show that the proposed method has a significant performance improvement in terms of predictive accuracy and numerical stability.

Keywords

Tool wear prediction deep learning deep multi-task learning sparse feature learning

Get full access to this article

View all access options for this article.

References

Colantonio

Equeter

Dehombreux

, et al. A systematic literature review of cutting tool wear monitoring in turning by using artificial intelligence techniques. Machines 2021; 9(12): 351.

Madhusudana

Gangadhar

Kumar

, et al. Use of discrete wavelet features and support vector machine for fault diagnosis of face milling tool. Struct Durab Health Monit 2018; 12(2): 111.

Lei

Zhou

Sun

, et al. An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. Int J Adv Manuf Technol 2020; 106(3–4): 1203–1212.

Meng

Zhang

Xiao

, et al. Tool wear prediction in milling based on a GSA-bp model with a multisensor fusion method. Int J Adv Manuf Technol 2021; 114(11–12): 3793–3802.

Soualhi

Nguyen

KTP

Medjaher

. Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mech Syst Signal Process 2020; 142: 106680.

Hinton

Salakhutdinov

. Reducing the dimensionality of data with neural networks. Science 2006; 313(5786): 504–507.

Goodfellow

Bengio

Courville

. Deep learning. Cambridge, MA: MIT Press, 2016.

Lei

Yang

Jiang

, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 2020; 138: 106587.

Wang

Zhong

, et al. Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism. Measurement 2021; 177: 109254.

10.

Shi

Xuan

, et al. Research on tool wear prediction based on temperature signals and deep learning. Wear 2021; 478–479: 203902.

11.

Marani

Zeinali

Songmene

, et al. Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling. Measurement 2021; 177: 109329.

12.

Zhang

Wang

, et al. Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction. Int J Adv Manuf Technol 2021; 114(9–10): 2651–2675.

13.

Dou

Jiao

, et al. An unsupervised online monitoring method for tool wear using a sparse auto-encoder. Int J Adv Manuf Technol 2020; 106(5–6): 2493–2507.

14.

Cao

Chen

Yao

, et al. Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification. Comput Ind 2019; 106: 71–84.

15.

Terrazas

Martínez-Arellano

Benardos

, et al. Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach. J Manuf Mater Process 2018; 2(4): 72.

16.

Cai

Zhang

, et al. A hybrid information model based on long short-term memory network for tool condition monitoring. J Intell Manuf 2020; 31(6): 1497–1510.

17.

Collobert

Weston

. A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, 2008, pp.160–167. New York: Association for Computing Machinery.

18.

Chang

Pan

Sun

, et al. Yoltrack: Multitask learning based real-time multiobject tracking and segmentation for autonomous vehicles. IEEE Trans Neural Netw Learn Syst 2021; 32(12): 5323–5333.

19.

Spinde

Krieger

Ruas

, et al. Exploiting transformer-based multitask learning for the detection of media bias in news articles. In: Proceedings of the iConference, 2022.

20.

Chen

Cao

Zhu

, et al. A multi-task learning approach for improving travel recommendation with keywords generation. Knowl Based Syst 2021; 233: 107521.

21.

Nguyen-Vo

Trinh

Nguyen

, et al. ICYP-MFE: identifying human cytochrome p450 inhibitors using multitask learning and molecular fingerprint-embedded encoding. J Chem Inf Model 2021.

22.

Kim

Sohn

. Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach. J Intell Manuf 2021; 32(8): 2169–2179.

23.

Wang

Qin

Liu

, et al. A new multitask learning method for tool wear condition and part surface quality prediction. IEEE Trans Ind Inform 2021; 17(9): 6023–6033.

24.

Liu

Wang

Liu

, et al. Multitask learning based on lightweight 1dcnn for fault diagnosis of wheelset bearings. IEEE Trans Instrum Meas 2021; 70: 1–11.

25.

Yao

Liu

, et al. Multi-task learning based encoder-decoder: a comprehensive detection and diagnosis system for multi-sensor data. Adv Mech Eng 2021; 13(5): 16878140211013138.

26.

Cao

Bian

Mao

, et al. Fault diagnosis based on domain adaptive multi-task learning convolutional neural network. In: 2021 CAA symposium on fault detection, supervision, and safety for technical processes (SAFEPROCESS), pp. 1–6. New York, NY: IEEE.

27.

Mao

Feng

, et al. Bearing fault diagnosis based on multi-task learning. In: 2018 Prognostics and system health management conference (PHM-Chongqing), pp.358–363. New York, NY: IEEE.

28.

Zhou

Chen

. Multi-task learning: Theory, algorithms, and applications. SDM tutorials. Tempe, AZ: The Biodesign Institute, 2012.

29.

Liu

. Multi-task feature learning via efficient l2, 1-norm minimization. arXiv preprint, arXiv:12052631, 2012.

30.

Kingma

. Adam: A method for stochastic optimization. arXiv preprint, arXiv:14126980, 2014.

31.

Ren

Cui

Sun

, et al. Multi-bearing remaining useful life collaborative prediction: a deep learning approach. J Manuf Syst 2017; 43: 248–256.

32.

Chang

Lin

. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2011; 2(3): 1–27.

33.

Snoek

Larochelle

Adams

. Practical bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 2012; 25: 2951–2959.

34.

Van der Maaten

Hinton

. Visualizing data using t-SNE. J Mach Learn Res 2008; 9(11): 2579–2605.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.39 MB