Application of Self-Learning for Tool Failure Diagnosis in Robotic Drilling System

Abstract The effort in monitoring tool condition for fully automatic drilling processes has resulted in the development of an intelligent drilling machine to work with industrial robots. This computer-controlled drilling machine has five built-in sensors. Based on the pattern recognition of sensor outputs with sets of algorithms, an intelligent diagnostic system has been developed for on-line detection of and pin-pointing of anyone of the nine drill failures: chisel edge wear, margin wear, breakage, flank wear, crater wear, lip height difference, corner wear and chipping at lips. Many inherent factors affecting the criteria for the judgement of drill wear/breakage, such as drill size, drill material, drill geometry, workpiece material, cutting speed, feed rate, material microstructure, hardness distribution, etc. is avoided by implementing, a self-learning system.