Tool wear state prediction based on feature-based transfer learning

Accurate identification of the tool wear state during the machining process is of great significance to improve product quality and benefit. The wear states of the same tool type and machining material have similarities during the machining process. By mining the data value of the historical machining process and analyzing the similarity of the procedure, the subsequent machining process can be predicted with the help of transfer learning. Therefore, this study proposes a tool wear prediction scheme based on feature-based transfer learning to realize the accurate prediction of the tool wear state. The genetic algorithm (GA) is used to select a subset of sensor features that are highly correlated with tool wear. Then, the source domain and target domain are constructed on the basis of the selected sensor features of the historical tool and the new tool during the machining process, respectively. In addition, features in the life cycle of the new tool are completed by feature-based transfer learning. After feature transfer, the maximum mean square discrepancy (MMD) method is used to evaluate the similarity of features, and the optimal feature subset is selected according to the evaluation result. Finally, the particle swarm-optimized support vector machine (PSO-SVM) model is applied to predict the tool wear states during the new tool machining. The effectiveness of the proposed tool wear scheme is verified by the cutting force and wear data of the tool life cycle under three different milling parameter combinations. Results with high accuracy show the advantages of the feature-based transfer learning method for tool wear state prediction.

[1]  Stanislaw Osowski,et al.  Transfer learning in recognition of drill wear using convolutional neural network , 2017, 2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE).

[2]  Alper Bastürk,et al.  Static facial expression recognition using convolutional neural networks based on transfer learning and hyperparameter optimization , 2020, Multim. Tools Appl..

[3]  Álisson Rocha Machado,et al.  A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission , 2015 .

[4]  Ning Li,et al.  Force-based tool condition monitoring for turning process using v-support vector regression , 2017 .

[5]  Zhibin Zhao,et al.  Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing , 2019, IEEE Transactions on Industrial Informatics.

[6]  S. Subramanian,et al.  Effect of dynamic recrystallization at tool-chip interface on accelerating tool wear during high-speed cutting of AISI1045 steel , 2016 .

[7]  T. Warren Liao,et al.  Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring , 2010, Eng. Appl. Artif. Intell..

[8]  Arif Gülten,et al.  Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases , 2013, Digit. Signal Process..

[9]  Tan Jianping,et al.  Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM. , 2020 .

[10]  Patrick Siarry,et al.  A postural information based biometric authentification system employing S-transform, radial basis network and Kalman filtering , 2010 .

[11]  Wentao Mao,et al.  Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning , 2020, IEEE Transactions on Instrumentation and Measurement.

[12]  María Teresa García-Ordás,et al.  Tool wear monitoring using an online, automatic and low cost system based on local texture , 2018, Mechanical Systems and Signal Processing.

[13]  Philip S. Yu,et al.  Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Farbod Akhavan Niaki,et al.  A comprehensive study on the effects of tool wear on surface roughness, dimensional integrity and residual stress in turning IN718 hard-to-machine alloy , 2017 .

[15]  T. Kurfess,et al.  Tool life predictions in milling using spindle power with the neural network technique , 2016 .

[16]  Jia Wang,et al.  Research on Building Fire Risk Fast Assessment Method Based on Fuzzy comprehensive evaluation and SVM , 2018 .

[17]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[18]  Ning Li,et al.  Hidden semi-Markov model-based method for tool wear estimation in milling process , 2017 .

[19]  Dahu Zhu,et al.  Tool wear characteristics in machining of nickel-based superalloys , 2013 .

[20]  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.

[21]  Alper Basturk,et al.  Static facial expression recognition using convolutional neural networks based on transfer learning and hyperparameter optimization , 2020, Multimedia Tools and Applications.

[22]  Durul Ulutan,et al.  A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials , 2016 .

[23]  Weiming Shen,et al.  A sensor fusion and support vector machine based approach for recognition of complex machining conditions , 2018, J. Intell. Manuf..

[24]  Syed Fahad Tahir,et al.  Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images , 2020, Int. J. Imaging Syst. Technol..

[25]  Connor Jennings,et al.  A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests , 2017 .

[26]  Rahul Pramanik,et al.  Segmentation-based recognition system for handwritten Bangla and Devanagari words using conventional classification and transfer learning , 2020, IET Image Process..

[27]  Dongdong Kong,et al.  Tool wear monitoring based on kernel principal component analysis and v-support vector regression , 2016, The International Journal of Advanced Manufacturing Technology.

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

[29]  Dongfeng Shi,et al.  Tool wear predictive model based on least squares support vector machines , 2007 .

[30]  Dongdong Kong,et al.  Tool Wear Estimation in End Milling of Titanium Alloy Using NPE and a Novel WOA-SVM Model , 2020, IEEE Transactions on Instrumentation and Measurement.

[31]  Marta Maslej,et al.  Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study , 2019, Journal of medical Internet research.

[32]  Julian Padget,et al.  Multi-sensor data fusion framework for CNC machining monitoring , 2016 .

[33]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[34]  Philip S. Yu,et al.  Adaptation Regularization: A General Framework for Transfer Learning , 2014, IEEE Transactions on Knowledge and Data Engineering.

[35]  Ping Liu,et al.  A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction , 2020, International journal of environmental research and public health.

[36]  Zakaria Elberrichi,et al.  Feature selection for text classification using genetic algorithms , 2016, 2016 8th International Conference on Modelling, Identification and Control (ICMIC).

[37]  Dilbag Singh,et al.  Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning , 2020, Journal of biomolecular structure & dynamics.

[38]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[39]  Yuxin Cui,et al.  Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation , 2018, Applied Sciences.

[40]  D. R. Salgado,et al.  Analysis of the structure of vibration signals for tool wear detection , 2008 .

[41]  Andrew P. Bradley,et al.  Rule extraction from support vector machines: A review , 2010, Neurocomputing.

[42]  Doriana M. D’Addona,et al.  Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms , 2020, Production Engineering.

[43]  Fabao Yan,et al.  Autonomous Martian rock image classification based on transfer deep learning methods , 2020, Earth Science Informatics.