KNOWLEDGE-BASED DIAGNOSIS OF DRILL CONDITIONS

被引:6
作者
HONG, SY [1 ]
机构
[1] WRIGHT STATE UNIV, DEPT MECH & MAT ENGN, DAYTON, OH 45435 USA
关键词
TOOL WEAR; SENSING; MACHINE INTELLIGENCE; DRILL FAILURE;
D O I
10.1007/BF00123967
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
One major bottleneck in the automation of the drilling process by robots in the aerospace industry is drill condition monitoring. This paper describes a system approach to solve this problem through the advancement of new machine design, sensor instrumentation, metal-cutting research, and intelligent software development. All drill failures can be detected and distinguished: chisel edge wear, flank wear, crater wear, margin wear, corner wear, breakage, asymmetry, lip height difference, and chipping at lips. However, in the real manufacturing environment, different workpiece materials, drill size, drill geometry, drill material, cutting speed, feed rate, etc. will change the criteria for judging the drill condition. The knowledge base used for diagnosing the drill failures requires a huge data bank and prior exhaustive testing. A self-learning scheme is therefore introduced to the machine in order to acquire the threshold history needed for automatic diagnosis by using the same new tool under the same drilling conditions.
引用
收藏
页码:233 / 241
页数:9
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