Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling

摘要

The paper presents the idea of using support vector machine algorithm in a tool wear identification system in chipboard drilling. The indirect sources of information about tool wear were: feed force, cutting torque, acceleration of jig vibration, audible noise, and ultrasonic acoustic emission signals. The drills were classified (analogous to traffic rules) as "Green" (able to work), "Yellow" (warning state) or "Red" (unable to work-replacement needed).