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Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions
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College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China, 325035
Shaoxing Customs, Shaoxing, China, 312099
School of Mechatronics and Transportation, Jiaxing Nanyang Polytechnic Institute, Jiaxing, China, 314031
Publication date: 2021-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(4):612-618
  • A data fusion- LSTM is proposed to estimate tool wear under different cutting conditions.
  • NCA is used to select useful features fusioned by EMD VMD and FSST.
  • Experimental results show the proposed method outperforms significantly SVR and RNN.
Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.
Cao XC, Chen BQ, Yao B, He WP. Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification. Computers in Industry 2019; 106: 71-84,
Dong W, Tan X. Bayesian Neighborhood Component Analysis. IEEE Transactions on Neural Networks & Learning Systems 2018;29(7): 3140-3151,
Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Transactions on Signal Processing 2014; 62(3): 531-544,
He K, Gao M, Zhao Z. Soft ComputingT echniques for Surface Roughness Prediction in Hard Turning: A LiteratureReview. IEEE Access 2019; 7: 89556-89569,
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation 1997; 9(8): 1735-80,
Hua YS, Mou LC, Zhu XX. Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification. Isprs Journal of Photogrammetry And Remote Sensing 2019; 149: 188-199,
Huang NE, Shen Z, Long SR, Wu MC, Shih HH,Zheng Q. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings Mathematical Physical & Engineering Sciences 1998, 454(1971): 903-995,
Huang Z, Zhu J, Lei J, Li X, Tian F. Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing 2020;31(4): 953-966,
Jasiulewicz-Kaczmarek M, Antosz K, Żywica P,Mazurkiewicz D, Sun B, Ren Y. Framework of machine criticality assessment with criteria interactions. Eksploatacja i Niezawodnosc - Maintenance andReliability 2021; 23(2): 207-220,
Kozlowski E, Mazurkiewicz D, Zabinski T,Prucnal S, Sep J. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability2019; 21(4): 679-685,
Kozłowski E, Mazurkiewicz D, Żabiński T,Prucnal S, Sęp J. Machining sensor data management for operation-level predictive model. Expert Systems with Applications 2020; 159: 1-22,
Kumar A, Gandhi CP, Zhou YQ, Kumar R, XiangJW. Improved CNN for the diagnosis of engine defects of 2-wheeler vehicle using wavelet synchro-squeezed transform (WSST). Knowledge based System 2020; 208,106453,
Kumar A, Gandhi CP, Zhou YQ, Kumar R, XiangJW. Variational mode decomposition based symmetric single valued neutrosophiccross entropy measure for the identification of bearing defects in acentrifugal pump. Applied Acoustics 2020; 165, 107294,
Kumar A, Kumar R. Adaptive artificial intelligence for automatic identification of defect in the angular contact bearing. Neural Computing & Applications 2018; 29: 277-287,
Lei Z, Zhou YQ, Sun BT, Sun WF. An intrinsic time scale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. International Journal of Advanced Manufacturing Technology 2020; 106(3-4): 1203-1212, https://doi. org/10.1007/s00170-019-04689-9.
Lei Z, Zhu QS, Zhou YQ, Sun BT, Sun WF, PanXM. A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimationin Milling Processes Based on Vibration Signals. International Journal ofPrecision Engineering and Manufacturing- Green Technology 2021; 8: 745-759,
Liu M, Wu W, Gu Z, Yu Z, Qi F, Li Y. Deep learning based on Batch Normalization for P300 signal detection. Neurocomputing 2018; 275: 288-297,
Ma X, Tao Z, Wang Y, Yu H, Wang Y. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C- Emerging Technologies2015; 54: 187-197,
Musavi SH, Davoodi B, Eskandari B. Evaluation of surface roughness and optimization of cutting parameters in turning of AA2024 alloy under different cooling-lubrication conditions using RSM method. Journal of Central South University 2020, 27(6): 1714- 1728,
Raghu S, Sriraam N. Classification of focaland non-focal EEG signals using neighborhood component analysis and machine learning algorithms. Expert Systems with Applications 2018; 113: 18-32,
Rosienkiewicz M. Artificial intelligence-based hybrid forecasting models for manufacturing systems. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23 (2): 263-277,
Sedlacek M, Podgornik B, Vizintin J. Influence of surface preparation on roughness parameters friction and wear. Wear 2009; 266(3-4): 482-487,
Siddhpura A, Paurobally R. A review of flankwear prediction methods for tool condition monitoring in a turning process. International Journal of Advanced Manufacturing Technology 2013; 65(1-4):371-393,
Tao Z, An Q, Liu G, Chen M. A novel methodfor tool condition monitoring based on long short-term memory and hidden Markov model hybrid framework in high-speed milling Ti-6Al-4V. International Journalof Advanced Manufacturing Technology 2019; 105(7-8): 3165-3182,
Tary JB, Herrera RH, Baan MV. Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms. Philosophical Transactions of the Royal Society A 2018; 376(2126), 20170254,
Tim VH, Chris KM. Self-supervised learning for tool wear monitoring with a disentangled-variational- autoencoder. International Journal of Hydromechatronics 2021; 4(1): 69-98,
Wang B, Liu Z. Influences of tool structure tool material and tool wear on machined surface integrity during turning and milling of titanium and nickel alloys: a review. International Journal of Advanced Manufacturing Technology 2018; 98(5-8): 1925-1975,
Zhang X, Zhao J. Compound fault detection in gearbox based on time synchronous resample and adaptive variational mode decomposition. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020;22(1): 161-169, http://dxdoiorg/1017531/ein202....
Zhao R, Yan R, Wang J, Mao K. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors 2017; 17(2), 273,
Zhi GF, He DD, Sun WF, Zhou YQ, Pan XM, Gao C. An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples. Measurement Science and Technology 2021; 32(6), 064006,
Zhou JT, Zhao X, Gao J. Tool remaining usefullife prediction method based on LSTM under variable working conditions. International Journal of Advanced Manufacturing Technology 2019; 104(9-12): 4715-4726,
Zhou YQ, Sun BT, Sun WF. A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling. Measurement 2020; 166: 108186,
Zhou YQ, Sun BT, Sun WF, Lei Z. Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process. Journal of Intelligent Manufacturing 2020,
Zhou YQ, Xue W. Review of tool condition monitoring methods in milling processes. International Journal of Advanced Manufacturing Technology 2018; 96(5-8): 2509-2523,
Zhu QS, Zhou YQ, Sun BT, He DD, Sun WF. A tool wear condition monitoring approach for end milling based on numerical simulation. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(2):371-380,
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