Self-learning system for knowledge-based diagnoses of drill condition in fully automatic manufacturing system

Shane Y. Hong

doi:10.1117/12.56885

1 March 1992 Self-learning system for knowledge-based diagnoses of drill condition in fully automatic manufacturing system

Shane Y. Hong

Author Affiliations +

Proceedings Volume 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems; (1992) https://doi.org/10.1117/12.56885
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

One major bottleneck in the automation of the drilling process by robots in the aerospace industry is the drill condition monitoring. The effort in solving this problem 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 any one of the nine drill failures: chisel edge wear, margin wear, breakage, flank wear, crater wear, lip height difference, corner wear, and chip at lips. However, the complexity of the manufacturing process has many inherent factors which affect the criteria for the judgment of drill wear/breakage, such as drill size, drill material, drill geometry, workpiece material, cutting speed, feedrate, material microstructure, hardness distribution, etc. The self-learning system is therefore required and has been implemented, enabling the machine to acquire the knowledge needed to judge the drill condition regardless of the complex situation.

Citation Download Citation

Shane Y. Hong "Self-learning system for knowledge-based diagnoses of drill condition in fully automatic manufacturing system", Proc. SPIE 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems, (1 March 1992); https://doi.org/10.1117/12.56885

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