Abstract
This paper describes a sensor-assisted method for on-line flank wear estimation in turning processes. This method, unlike most of the existing ones, predicts continually the gradually increasing flank wear on a cutting tool. This method is tested on individual as well as different combinations of force, vibration, and acoustic emission sensors. The performance of the flank wear estimation method is studied by conducting extensive turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. The average percent flank wear estimation error for different sensor combinations, for the range of operating conditions that were used during neural network training, is between -0.91% and 12.97%. The results indicate that the proposed estimation method provides flank wear estimates good enough for most real-world turning processes.
Original language | English (US) |
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Pages | 461-466 |
Number of pages | 6 |
State | Published - Dec 1 1997 |
Event | Proceedings of the 1997 6th Annual Industrial Engineering Research Conference, IERC - Miami Beach, FL, USA Duration: May 17 1997 → May 18 1997 |
Other
Other | Proceedings of the 1997 6th Annual Industrial Engineering Research Conference, IERC |
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City | Miami Beach, FL, USA |
Period | 5/17/97 → 5/18/97 |
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering