Characterization of Tool Wear Measurement with Relation to the Surface Roughness in Turning

Abstract In turning, an accurate gauging of tool wear condition is an essential part of process control due to adverse effects on dimensional tolerance and surface finish quality. When the surface roughness is the primary concern, the conventional measure of tool wear is found to be imprecise because it provides very little information on the wear patterns in tool nose and flank. A tool wear model, developed in this study, represents the wear condition more comprehensively and accurately with relation to the surface roughness. Experimental results validate the model, showing 92% accuracy between the predicted surface roughness and the actual measurements.

[1]  Robert I. King,et al.  Handbook of High-Speed Machining Technology , 1986 .

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

[3]  Min-Yang Yang,et al.  A Study on the Relationships Between Static/Dynamic Cutting Force Components and Tool Wear , 2001 .

[4]  E. Armarego,et al.  The Machining of Metals , 1969 .

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

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

[7]  K. Ramamurthi,et al.  Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations , 1993 .

[8]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[9]  S. B. Rao,et al.  Tool Wear Monitoring Through the Dynamics of Stable Turning , 1986 .

[10]  Behnam Malakooti,et al.  In-process regressions and adaptive multicriteria neural networks for monitoring and supervising machining operations , 1995, J. Intell. Manuf..

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

[12]  Chen Zhou,et al.  Tool Status Recording and Its Use in Probabilistic Optimization , 1992 .

[13]  Toshiyuki Obikawa,et al.  Autonomous turning operation planning with adaptive prediction of tool wear and surface roughness , 1993 .

[14]  Jerzy Jedrzejewski,et al.  An approach to integrating intelligentdiagnostics and supervision of machine tools , 1998, J. Intell. Manuf..

[15]  X. D. Fang,et al.  A NEW ALGORITHM FOR DEVELOPING A REFERENCE-BASED MODEL FOR PREDICTING SURFACE ROUGHNESS IN FINISH MACHINING OF STEELS , 1997 .

[16]  Nidal Abu-Zahra,et al.  Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves , 2003 .

[17]  D. S. Ermer,et al.  Optimization of Multipass Turning With Constraints , 1981 .

[18]  David Vernon,et al.  Machine vision - automated visual inspection and robot vision , 1991 .

[19]  Warren R. DeVries,et al.  Analysis of Material Removal Processes , 1991 .