Tool Wear Estimation for Different Workpiece Materials Using the Same Monitoring System

Abstract This paper presents a tool wear monitoring system that uses the same signals and prediction strategy for monitoring the machining process of different materials, i.e., a steel and an aluminium alloy. It is an important requirement for a monitoring system to be applied in real applications. Experiments have been performed on a lathe over a range of different cutting conditions, and TiN coated tools were used. The monitoring signals used are the AC feed drive motor current and the cutting vibrations. The geometry tool parameters used as inputs are the tool angle and the radius. The performance of the proposed system was validated against different experiments. In particular, different tests were performed using different numbers of experiments obtaining a rmse for tool wear estimation of 17.63 μm and 13.45 μm for steel and aluminium alloys respectively.

[1]  Ch Srinivasa Rao,et al.  Tool wear monitoring—an intelligent approach , 2004 .

[2]  Andrew L. Rukhin,et al.  Analysis of Time Series Structure SSA and Related Techniques , 2002, Technometrics.

[3]  D. R. Salgado,et al.  Tool wear detection in turning operations using singular spectrum analysis , 2006 .

[4]  Chatchapol Chungchoo,et al.  On-line tool wear estimation in CNC turning operations , 2001 .

[5]  Xiaoli Li,et al.  Real-time tool wear condition monitoring in turning , 2001 .

[6]  Xiaoli Li,et al.  Development of current sensor for cutting force measurement in turning , 2005, IEEE Transactions on Instrumentation and Measurement.

[7]  P. S. Heyns,et al.  An industrial tool wear monitoring system for interrupted turning , 2004 .

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

[9]  Krzysztof Jemielniak.  Tool Wear Monitoring Based on a Non-Monotonic Signal Feature , 2006 .

[10]  D. R. Salgado,et al.  An approach based on current and sound signals for in-process tool wear monitoring , 2007 .

[11]  Issam Abu-Mahfouz,et al.  Drilling wear detection and classification using vibration signals and artificial neural network , 2003 .

[12]  Fritz Klocke,et al.  Development of a tool wear-monitoring system for hard turning , 2003 .

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

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

[15]  Tae-Yong Kim,et al.  Adaptive cutting force control for a machining center by using indirect cutting force measurements , 1996 .