An unsupervised online monitoring method for tool wear using a sparse auto-encoder

Tool wear, and its online monitoring, plays an important role in increasing productivity and improving product quality. We describe an unsupervised method to monitor the wear state of milling cutter by tracking an error sequence generated by reconstructing monitoring signals from a sparse auto-encoder (SAE). The monitoring signals consist of the force and vibration signals collected during the cutting process. We establish a well-structured SAE model, which can adaptively extract the characteristics of the signal and complete the training of the model without supervision of the empirical label and investigate the reconstruction performance of the model for cutting signal. On this basis, an automatic online tool wear state identification strategy is designed to monitor the milling process. The mean reconstruction error (MRE) sequence associated with tool wear is recorded in real time by reconstructing the next signal segment from the SAE model, which is trained and updated using the current signal segment. Monitoring criteria and thresholds are recommended to automate the identification of tool wear conditions based on the filtered MRE curve. Five experiments with two different milling environments are run to confirm the feasibility of tool wear monitoring using this method, and the results show that the method can be used to monitor tool wear conditions online under different milling conditions without being supervised by any empirical labels.

[1]  Roshun Paurobally,et al.  A review of flank wear prediction methods for tool condition monitoring in a turning process , 2012, The International Journal of Advanced Manufacturing Technology.

[2]  Nan Jiang,et al.  An empirical analysis of different sparse penalties for autoencoder in unsupervised feature learning , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[3]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[4]  Tae Hyung Kim,et al.  A two-step feature selection method for monitoring tool wear and its application to the coroning process , 2013 .

[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]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[7]  Bin Zhang,et al.  Robust Tool Wear Monitoring Using Systematic Feature Selection in Turning Processes With Consideration of Uncertainties , 2018, Journal of Manufacturing Science and Engineering.

[8]  Wennian Yu,et al.  Cutting Tool Wear Estimation Using a Genetic Algorithm Based Long Short-Term Memory Neural Network , 2018 .

[9]  Barry K. Fussell,et al.  Real-time tool wear monitoring in milling using a cutting condition independent method , 2015 .

[10]  Carlos Henrique Lauro,et al.  Monitoring and processing signal applied in machining processes – A review , 2014 .

[11]  George Panoutsos,et al.  Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing , 2019, IEEE Transactions on Industrial Electronics.

[12]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[13]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[14]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[15]  A. I. Azmi,et al.  Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites , 2015, Adv. Eng. Softw..

[16]  Sam Turner,et al.  Tool wear monitoring using naïve Bayes classifiers , 2014, The International Journal of Advanced Manufacturing Technology.

[17]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[18]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Sebastian Thiede,et al.  Tool Wear and Surface Quality Monitoring Using High Frequency CNC Machine Tool Current Signature , 2018, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN).

[20]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[21]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[22]  Kunpeng Zhu,et al.  Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations , 2018, IEEE Transactions on Industrial Informatics.

[23]  Rui Liu,et al.  Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling , 2017, The International Journal of Advanced Manufacturing Technology.

[24]  Durul Ulutan,et al.  In-Process Tool Flank Wear Estimation in Machining Gamma-Prime Strengthened Alloys Using Kalman Filter , 2015 .

[25]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[26]  Eduardo Carlos Bianchi,et al.  Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics , 2015, Expert Syst. Appl..

[27]  Surjya K. Pal,et al.  Progressive tool flank wear monitoring by applying discrete wavelet transform on turned surface images , 2016 .

[28]  Elijah Kannatey-Asibu,et al.  Monitoring tool wear using classifier fusion , 2017 .

[29]  Yuxuan Chen,et al.  Predicting tool wear with multi-sensor data using deep belief networks , 2018, The International Journal of Advanced Manufacturing Technology.

[30]  Robert Lewis Reuben,et al.  The use of cutting force and acoustic emission signals for the monitoring of tool insert geometry during rough face milling , 1997 .

[31]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[32]  Geok Soon Hong,et al.  Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results , 2009 .

[33]  Geoffrey E. Hinton,et al.  Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.

[34]  Marc Thomas,et al.  Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process , 2018, The International Journal of Advanced Manufacturing Technology.

[35]  D. E. Dimla,et al.  On-line metal cutting tool condition monitoring.: I: force and vibration analyses , 2000 .

[36]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[37]  Zhicheng Shi,et al.  Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method , 2018 .

[38]  Svetan Ratchev,et al.  Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach , 2018, Journal of Manufacturing and Materials Processing.