ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH

Abstract The supervision of tool wear is the most difficult task in the context of tool condition monitoring for metal-cutting processes. Based on a continuous acquisition of signals with multi-sensor systems it is possible to estimate or to classify certain wear parameters by means of neural networks. However, despite of more than a decade of intensive scientific research, the development of tool wear monitoring systems is an on-going attempt. This article aims to investigate, why it has not been possible to develop appropriate monitoring systems up to now. In order to describe the ‘state of the art’, 138 publications dealing with on-line and indirect tool wear monitoring in turning by means of artificial neural networks are evaluated. The article compares the methods applied in these publications as well as the methodologies used to select certain methods, to carry out simulation experiments, to evaluate and to present results, etc. As a conclusion, possible directions for future research in this area are pointed out. Many of the recommendations are valid for other machining processes using tools with or without defined cutting edges, too.

[1]  P. S. Heyns,et al.  Synthetic diamond tool wear monitoring using vibration measurements , 2000 .

[2]  L. Sachs.  Angewandte Statistik : Anwendung statistischer Methoden , 1984 .

[3]  D. E. Dimla,et al.  Automatic tool state identification in a metal turning operation using MLP neural networks and multivariate process parameters , 1998 .

[4]  S. Iyengar,et al.  Multi-Sensor Fusion: Fundamentals and Applications With Software , 1997 .

[5]  David Dornfeld,et al.  DESIGN AND IMPLEMENTATION OF SENSOR-BASED TOOL-WEAR MONITORING SYSTEMS , 1996 .

[6]  Kourosh Danai,et al.  On-line tool breakage detection in turning: a multi-sensor method , 1994 .

[7]  S. Y. Liang,et al.  Tool Wear and Chatter Detection in Turning via Time-Series Modeling and Frequency Band Averaging , 1994 .

[8]  Geok Soon Hong,et al.  Using neural network for tool condition monitoring based on wavelet decomposition , 1996 .

[9]  S. Li,et al.  Tool Condition Monitoring in Machining by Fuzzy Neural Networks , 1996 .

[10]  A. Galip Ulsoy,et al.  Tool Wear and Breakage Detection Using a Process Model , 1986 .

[11]  Laura I. Burke,et al.  Tool condition monitoring in metal cutting: A neural network approach , 1991, J. Intell. Manuf..

[12]  MengChu Zhou,et al.  On-line robust identification of tool-wear via multi-sensor neural-network fusion , 1998 .

[13]  V.T. Sunil Elanagar,et al.  Design and implementation of tool wear monitoring with radial basis function neural networks , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[14]  J. H. Lee,et al.  One-step-ahead prediction of flank wear using cutting force , 1999 .

[15]  P. M. Lister,et al.  A multi-sensor integration method of signals in a metal cutting operation via application of multilayer perceptron neural networks , 1997 .

[16]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[17]  Lawrence A. Klein,et al.  Sensor and Data Fusion Concepts and Applications , 1993 .

[18]  R. G. Khanchustambham,et al.  A neural network approach to on-line monitoring of a turning process , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[19]  Bernhard Sick.  Online tool wear classification in turning with time-delay neural networks and process-specific pre-processing , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[20]  Bernhard Sick.  Monitoring the wear of cutting tools in CNC-lathes with artificial neural networks , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[21]  Ruxu Du,et al.  Automated Monitoring of Manufacturing Processes, Part 2: Applications , 1995 .

[22]  Anna Zawada-Tomkiewicz,et al.  Classifying the wear of turning tools with neural networks , 2001 .

[23]  Ping Yi Chao,et al.  An improved neural network model for the prediction of cutting tool life , 1997, J. Intell. Manuf..

[24]  Gerald Warnecke,et al.  Control of tolerances in turning by predictive control with neural networks , 1998, J. Intell. Manuf..

[25]  R. K Dutta,et al.  Applicability of the modified back-propagation algorithm in tool condition monitoring for faster convergence , 2000 .

[26]  Guy Littlefair,et al.  Fusion of integrated multisensor data for tool wear monitoring , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[27]  Judith E. Dayhoff,et al.  Adaptive time-delay neural network for temporal correlation and prediction , 1992, Other Conferences.

[28]  Vijay K. Jain,et al.  On-line monitoring of tool wear in turning using a neural network , 1999 .

[29]  Toshiyuki Obikawa,et al.  Tool wear monitoring for optimizing cutting conditions , 1996 .

[30]  Yoke San Wong,et al.  An intelligent sensor system approach for reliable tool flank wear recognition , 1998 .

[31]  Li Dan,et al.  Tool wear and failure monitoring techniques for turning—A review , 1990 .

[32]  Eberhard Hänsler,et al.  Statistische Signale, Grundlagen und Anwendungen , 1991 .

[33]  Jack Jeswiet,et al.  On-line wear estimation using neural networks , 1998 .

[34]  Ulrich Bodenhausen.  Learning internal representations of pattern sequences in a neural network with adaptive time-delays , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[35]  D. E. Dimla,et al.  Investigation of a single-layer perceptron neural network to tool wear inception in a metal turning process , 1996 .

[36]  Y. G. Srinivasa,et al.  A back-propagation algorithm applied to tool wear monitoring , 1994 .

[37]  Geok Soon Hong,et al.  On-line cutting state recognition in turning Using a neural network , 1995 .

[38]  Geok Soon Hong,et al.  Application of Kohonen neural network for tool condition monitoring , 1995, Other Conferences.

[39]  D. E. Dimla,et al.  Neural network solutions to the tool condition monitoring problem in metal cutting—A critical review of methods , 1997 .

[40]  Behnam Bahr,et al.  Sensor fusion for monitoring machine tool conditions , 1997 .

[41]  Satish T. S. Bukkapatnam,et al.  Analysis of acoustic emission signals in machining , 1999 .

[42]  S. Li,et al.  FUZZY CLUSTERING FOR AUTOMATED TOOL CONDITION MONITORING IN MACHINING , 1996 .

[43]  George Chryssolouris,et al.  Sensor Synthesis for Control of Manufacturing Processes , 1992 .

[44]  Bernhard Sick,et al.  Recurrent and non-recurrent dynamic network paradigms: a case study , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[45]  Rui Silva,et al.  TOOL WEAR MONITORING OF TURNING OPERATIONS BY NEURAL NETWORK AND EXPERT SYSTEM CLASSIFICATION OF A FEATURE SET GENERATED FROM MULTIPLE SENSORS , 1998 .

[46]  MengChu Zhou,et al.  Design of artificial neural networks for tool wear monitoring , 1997, J. Intell. Manuf..

[47]  A. Sicheneder,et al.  A framework for the graphical specification and execution of complex signal processing applications , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[48]  K. N. Prasad,et al.  Tool wear evaluation by stereo vision and prediction by artificial neural network , 2001 .

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

[50]  Hilary Buxton,et al.  Learning Gestures with Time-Delay RBF Networks , 1998 .

[51]  Andrew Y. C. Nee,et al.  A Comprehensive Identification of Tool Failure and Chatter Using a Parallel Multi-ART2 Neural Network , 1998 .

[52]  Jack Jeswiet,et al.  Real time implementation of on-line tool condition monitoring in turning , 1999 .

[53]  Sagar V. Kamarthi,et al.  Wavelet networks for sensor signal classification in flank wear assessment , 1998, J. Intell. Manuf..

[54]  D. Guinea,et al.  An empirical multi-sensor estimation of tool wear , 1993 .

[55]  M. F. DeVries,et al.  Neural Network Sensor Fusion for Tool Condition Monitoring , 1990 .

[56]  Richard Colbaugh,et al.  Real-time tool wear estimation using recurrent neural networks , 1995, Proceedings of Tenth International Symposium on Intelligent Control.

[57]  Stuart E. Dreyfus,et al.  A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear , 1995 .

[58]  Santanu Das,et al.  Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach , 1996, Neural Networks.

[59]  Kourosh Danai,et al.  Unsupervised Neural Network for Tool Breakage Detection in Turning , 1993 .

[60]  S. S. Rangwala,et al.  Machining process characterization and intelligent tool condition monitoring using acoustic emission signal analysis , 1994 .

[61]  M. C. Shaw.  Metal Cutting Principles , 1960 .

[62]  Satish T. S. Bukkapatnam,et al.  Fractal Estimation of Flank Wear in Turning , 2000 .

[63]  D. E. Dimla,et al.  Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods , 2000 .

[64]  D. E. Dimla.  A modular tool wear monitoring system in a metal cutting operation using MLP neural networks and multivariate process parameters , 1998 .

[65]  Bernhard Sick.  Chances and Risks of Sensor Fusion with Neural Networks: An Application Example , 1998 .

[66]  R. J. Kuo,et al.  Intelligent tool wear estimation system through artificial neural networks and fuzzy modeling , 1998, Artif. Intell. Eng..

[67]  Vijay K. Jain,et al.  On-Line Monitoring of Tool Wear and Control of Dimensional Inaccuracy in Turning , 2001 .

[68]  Sun-Yuan Kung,et al.  A delay damage model selection algorithm for NARX neural networks , 1997, IEEE Trans. Signal Process..

[69]  Bernhard Sick,et al.  Using temporal information in input features of neural networks , 1999 .

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

[71]  László Monostori,et al.  A Step towards Intelligent Manufacturing: Modelling and Monitoring of Manufacturing Processes through Artificial Neural Networks , 1993 .

[72]  Yung C. Shin,et al.  Robust Tool Wear Estimation With Radial Basis Function Neural Networks , 1995 .

[73]  E. L. Hines,et al.  Tool-wear prediction using artificial neural networks , 1995 .

[74]  R. J. Kuo,et al.  Intelligent precision flexible manufacturing system through artificial neural networks and fuzzy modeling , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[75]  Rajkumar Roy,et al.  Evaluation of wear of turning carbide inserts using neural networks , 1996 .

[76]  Bernhard Sick.  Online tool wear monitoring in turning using time-delay neural networks , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[77]  Laura I. Burke,et al.  An unsupervised neural network approach to tool wear identification , 1993 .

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

[79]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[80]  Krzysztof Jemielniak,et al.  Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network , 1998, J. Intell. Manuf..

[81]  Ruxu Du,et al.  Artificial Neural Network Architecture Design Using Similarity Measure With Applications in Engineering Monitoring and Diagnosis , 1996 .

[82]  D. E. Dimla,et al.  Tool condition monitoring in metal cutting through application of MLP neural networks , 1997 .

[83]  D. E. Dimla.  Artificial neural networks approach to tool condition monitoring in a metal turning operation , 1999, 1999 7th IEEE International Conference on Emerging Technologies and Factory Automation. Proceedings ETFA '99 (Cat. No.99TH8467).

[84]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[85]  David Dornfeld,et al.  Tool Wear Detection Using Time Series Analysis of Acoustic Emission , 1989 .

[86]  László Monostori,et al.  Wear estimation and state classification of cutting tools in turning via artificial neural networks , 1993 .

[87]  Bernhard Sick.  Classifying the Wear of Turning Tools with Neural Networks , 1997, ICANN.

[88]  Laura I. Burke,et al.  Competitive learning based approaches to tool-wear identification , 1992, IEEE Trans. Syst. Man Cybern..

[89]  Ruxu Du,et al.  Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods , 1995 .

[90]  C. S. Leem.  A practical monitoring strategy for machining process control , 1997 .

[91]  Y. G. Srinivasa,et al.  A procedure for training an artificial neural network with application to tool wear monitoring , 1998 .

[92]  Igor Grabec,et al.  Monitoring manufacturing processes by utilizing empirical modeling , 1998 .

[93]  Qiang Liu,et al.  On-line monitoring of flank wear in turning with multilayered feed-forward neural network , 1999 .

[94]  A. Chattopadhyay,et al.  Neural-networks-based tool wear monitoring in turning medium carbon steel using a coated carbide tool , 1997 .

[95]  Dayeong Kim,et al.  APPLICATION OF NEURAL NETWORKS TO FLANK WEAR PREDICTION , 1996 .

[96]  D. E. Dimla,et al.  On-line metal cutting tool condition monitoring.: II: tool-state classification using multi-layer perceptron neural networks , 2000 .

[97]  László Monostori,et al.  Cutting tool monitoring in turning under varying cutting conditions: an artificial neural network approach , 1993 .

[98]  Yung C. Shin,et al.  Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems , 1994, IEEE Trans. Neural Networks.

[99]  R. Teti.  A review of tool condition monitoring literature data base , 1995 .

[100]  Li Pheng Khoo,et al.  Tool condition monitoring using reflectance of chip surface and neural network , 2000, J. Intell. Manuf..

[101]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[102]  R J. Kuo,et al.  Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network , 1999, Neural Networks.

[103]  Yongyue Zhang,et al.  Detection of tool breakage in turning operations by using neural networks , 1995, Other Conferences.

[104]  MengChu Zhou,et al.  Monitoring of tool wear using artificial neural networks , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[105]  Geok Soon Hong,et al.  A new tool life criterion for tool condition monitoring using a neural network , 1995 .

[106]  Geok Soon Hong,et al.  Multi-Category Classification of Tool Conditions Using Wavelet Packets and ART2 Network , 1998 .

[107]  Bernhard Sick,et al.  The FFT butterfly operation in 4 processor cycles on a 24 bit fixed-point DSP with a pipelined multiplier , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[108]  D. L. Hall,et al.  Mathematical Techniques in Multisensor Data Fusion , 1992 .

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

[110]  X Q Li,et al.  Intelligent tool wear identification based on optical scattering image and hybrid artificial intelligence techniques , 1999 .

[111]  E. Brigham,et al.  The fast Fourier transform and its applications , 1988 .

[112]  Rui Silva,et al.  THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM UNDER CHANGING CUTTING CONDITIONS , 2000 .

[113]  D. E. Dimla,et al.  Application of a Back-Propagation Neural Network to Tool Condition Monitoring in a Metal Turning Process , 1997 .

[114]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[115]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[116]  Guy Littlefair,et al.  On-line tool condition monitoring using artificial neural networks , 1996 .

[117]  Sabbir Rangwala,et al.  A study of acoustic emission generated during orthogonal metal cutting—1: Energy analysis , 1991 .

[118]  Oren Masory,et al.  Detection of tool wear using multisensor readings defused by artificial neural network , 1991 .

[119]  A. Galip Ulsoy,et al.  DYNAMIC STATE MODEL FOR ON-LINE TOOL WEAR ESTIMATION IN TURNING. , 1987 .

[120]  Yung C. Shin,et al.  Modeling of Tool Forces for Worn Tools: Flank Wear Effects , 1996 .

[121]  P. M. Lister,et al.  Sensor Fusion for Cutting Tool State Identification in Metal Turning Through the Application of Perceptron Neural Networks , 1997 .

[122]  Marco Gori,et al.  Adaptive Processing of Sequences and Data Structures , 1998, Lecture Notes in Computer Science.

[123]  Sagar Kamarthi,et al.  FOURIER AND WAVELET TRANSFORM FOR FLANK WEAR ESTIMATION — A COMPARISON , 1997 .