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Proceedings Paper

Self-learning system for knowledge-based diagnoses of drill condition in fully automatic manufacturing system
Author(s): Shane Y. Hong
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Paper 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.

Paper Details

Date Published: 1 March 1992
PDF: 12 pages
Proc. SPIE 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems, (1 March 1992); doi: 10.1117/12.56885
Show Author Affiliations
Shane Y. Hong, Wright State Univ. (United States)

Published in SPIE Proceedings Vol. 1707:
Applications of Artificial Intelligence X: Knowledge-Based Systems
Gautam Biswas, Editor(s)

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