Fusion of hard and soft computing techniques in indirect, online tool wear monitoring

Indirect, online tool wear monitoring is one of the most difficult tasks in the context of process monitoring for metal-cutting machining processes. Based on a continuous acquisition of certain process parameters (signals such as cutting forces or acoustic emission) with multi-sensor systems, it is possible to estimate or to classify certain wear parameters. However, despite of intensive scientific research during the past decades, the development of reliable and flexible tool wear monitoring systems is an ongoing attempt. This article introduces a new, hybrid technique for tool wear monitoring in turning which fuses a physical process model (hard computing) with a neural network model (soft computing). The physical model describes the influence of cutting conditions (such as tool geometry or work material) on measured force signals and it is used to normalize these force signals. The neural model establishes a relationship between the normalized force signals and the wear state of the tool. The advantages of this approach are demonstrated by means of experimental results. Moreover, it is shown that the consideration of process parameters, cutting conditions, and wear in one model (either physical or neural) is extremely difficult and that existing hybrid approaches are not adequate. The ideas presented in this article can be transferred to many other process monitoring tasks.

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

[2]  E. Usui,et al.  Analytical Prediction of Three Dimensional Cutting Process , 1978 .

[3]  B. F. von Turkovich,et al.  A Comprehensive Tool Wear Model , 1986 .

[4]  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).

[5]  Y. Koren.  Flank Wear Model of Cutting Tools Using Control Theory , 1978 .

[6]  N. Suh,et al.  Tool Wear by Solution: A Quantitative Understanding , 1980 .

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

[8]  Werner Grass,et al.  Multisensorfusion bei der Verarbeitung mikrosystembasierter Signale , 1999, Informationstechnik Tech. Inform..

[9]  A. Galip Ulsoy,et al.  STATE MODEL FOR FLANK WEAR IN METAL CUTTING. , 1984 .

[10]  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.

[11]  Inyong Ham,et al.  Analysis of Tool Wear—Part I: Theoretical Models of Flank Wear , 1969 .

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

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

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

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

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

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

[18]  Gang Yu,et al.  Analytical model for tool wear monitoring in turning operations using ultrasound waves , 2000 .

[19]  Rui Silva,et al.  Cutting tool condition monitoring of the turning process using artificial intelligence , 1997 .

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

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

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

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

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

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

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

[27]  Z. Pálmai.  A new, physically defined function to describe the wear of cutting tools , 1974 .

[28]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .

[29]  T. Kitagawa,et al.  Analytical Prediction of Three Dimensional Cutting Process—Part 3: Cutting Temperature and Crater Wear of Carbide Tool , 1978 .

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

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

[32]  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.

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

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

[35]  Mukul Agarwal,et al.  Combining neural and conventional paradigms for modelling, prediction and control , 1997, Int. J. Syst. Sci..

[36]  E. Kannatey-Asibu.  A Transport-Diffusion Equation in Metal Cutting and its Application to Analysis of the Rate of Flank Wear , 1985 .

[37]  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.

[38]  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 .

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

[40]  Eric A. Wan,et al.  Finite impulse response neural networks with applications in time series prediction , 1994 .

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

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

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