Tool wear state recognition based on gradient boosting decision tree and hybrid classification RBM

Machined surface quality and dimensional accuracy are significantly affected by tool wear in machining process. Tool wear state (TWS) recognition is highly desirable to realize automated machining process. In order to improve the accuracy of TWS recognition, this research develops a TWS recognition scheme using an indirect measurement method which selects signal features that are strongly correlated with tool wear to recognize TWS. Firstly, three time domain features are proposed, including dynamic time warping feature and two entropy features. The time, frequency, and time-frequency domain features of the vibration and force signals are extracted to form a feature set. Secondly, gradient boosting decision tree (GBDT) is adopted to select the optimal feature subset. Lastly, contrastive divergence (CD) and RMSspectral are used to train hybrid classification RBM (H-ClassRBM). The trained H-ClassRBM is used for TWS recognition. The PHM challenge 2010 data set is used to validate the proposed scheme. Experimental results show that the proposed features have better monotonicity and correlation than the classical features. Compared with CD and Adadelta, CD and Adagrad, and CD and stochastic gradient descent with momentum, the H-ClassRBM trained by CD and RMSspectral improves recognition accuracy by 1%, 2%, and 2%, respectively. Compared with feedforward neural network, probabilistic neural network, Gaussian kernel support vector machine, and H-ClassRBM, the proposed TWS recognition scheme improves recognition accuracy by 37%, 51%, 9%, and 8%, respectively. Therefore, the proposed TWS recognition scheme is beneficial in improving the recognition accuracy of TWS, and provides an effective guide for decision-making in the machining process.

[1]  Mehrdad Nouri Khajavi,et al.  Milling tool wear diagnosis by feed motor current signal using an artificial neural network , 2016, Journal of Mechanical Science and Technology.

[2]  Mohamed Elhoseny,et al.  Feature selection based on artificial bee colony and gradient boosting decision tree , 2019, Appl. Soft Comput..

[3]  Zhiyang He,et al.  A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed , 2020 .

[4]  Volkan Cevher,et al.  Stochastic Spectral Descent for Discrete Graphical Models , 2016, IEEE Journal of Selected Topics in Signal Processing.

[5]  Feng Liu,et al.  Health condition assessment of ball bearings using TOSELM , 2018 .

[6]  Dongdong Kong,et al.  Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models , 2017, The International Journal of Advanced Manufacturing Technology.

[7]  Giovanna Martínez-Arellano,et al.  Tool wear classification using time series imaging and deep learning , 2019, The International Journal of Advanced Manufacturing Technology.

[8]  Hong Peng,et al.  An alternate method between generative objective and discriminative objective in training classification Restricted Boltzmann Machine , 2018, Knowl. Based Syst..

[9]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[10]  Bintao Sun,et al.  An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process , 2020 .

[11]  Zhengyou Xie,et al.  Feature selection and a method to improve the performance of tool condition monitoring , 2018, The International Journal of Advanced Manufacturing Technology.

[12]  Arindam Chaudhuri,et al.  The Minimization of Empirical Risk Through Stochastic Gradient Descent with Momentum Algorithms , 2019, CSOC.

[13]  Juan Lu,et al.  Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm , 2019, The International Journal of Advanced Manufacturing Technology.

[14]  P. S. Sastry,et al.  An Overview of Restricted Boltzmann Machines , 2019, Journal of the Indian Institute of Science.

[15]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[16]  Noureddine Zerhouni,et al.  Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics , 2015, IEEE Transactions on Industrial Electronics.

[17]  H. Iovu,et al.  New Biocompatible Mesoporous Silica/Polysaccharide Hybrid Materials as Possible Drug Delivery Systems , 2018, Materials.

[18]  Zhengguo Xu,et al.  A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW , 2019, IEEE Access.

[19]  Kunpeng Zhu,et al.  A generic tool wear model and its application to force modeling and wear monitoring in high speed milling , 2019, Mechanical Systems and Signal Processing.

[20]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[21]  Tao Mei,et al.  Online Condition Monitoring in Micromilling: A Force Waveform Shape Analysis Approach , 2015, IEEE Transactions on Industrial Electronics.

[22]  Bin Zhang,et al.  Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings , 2016, Qual. Reliab. Eng. Int..

[23]  Noureddine Zerhouni,et al.  Health assessment and life prediction of cutting tools based on support vector regression , 2015, J. Intell. Manuf..

[24]  Nirian Martín,et al.  Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator , 2017, Entropy.

[25]  Guo F Wang,et al.  Tool condition monitoring system based on support vector machine and differential evolution optimization , 2017 .

[26]  Razvan Pascanu,et al.  Learning Algorithms for the Classification Restricted Boltzmann Machine , 2012, J. Mach. Learn. Res..

[27]  Tien-I Liu,et al.  Tool condition monitoring (TCM) using neural networks , 2015 .

[28]  Jarosław Górski,et al.  Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling , 2019, European Journal of Wood and Wood Products.

[29]  Hui Wang,et al.  Modulation Signal Recognition Based on Information Entropy and Ensemble Learning , 2018, Entropy.

[30]  Paweł Twardowski,et al.  Prediction of Tool Wear Using Artificial Neural Networks during Turning of Hardened Steel , 2019, Materials.

[31]  Jiancheng Lv,et al.  Classification model of restricted Boltzmann machine based on reconstruction error , 2016, Neural Computing and Applications.

[32]  Yoshua Bengio,et al.  Equilibrated adaptive learning rates for non-convex optimization , 2015, NIPS.

[33]  Volkan Cevher,et al.  Preconditioned Spectral Descent for Deep Learning , 2015, NIPS.

[34]  Binqiang Chen,et al.  An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient , 2019, Applied Sciences.

[35]  Meiqing Wang,et al.  Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals , 2018 .

[36]  Meng Ma,et al.  A Deep Coupled Network for Health State Assessment of Cutting Tools Based on Fusion of Multisensory Signals , 2019, IEEE Transactions on Industrial Informatics.

[37]  Ning Li,et al.  Gaussian process regression for tool wear prediction , 2018 .