제어로봇시스템학회:학술대회논문집
- 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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- Pages.1570-1575
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- 1991
Development of tool condition monitoring system using unsupervised learning capability of the ART2 network
- Choii, Gi-Sang (Dept. of Control and Instrumentation Engineering, Seoul City University)
- 발행 : 1991.10.01
초록
The feasibility of using an adaptive resonance network (ART2) with unsupervised learning capability for too] wear detection in turning operations is investigated. Specifically, acoustic emission (AE) and cutting force signals were measured during machining, the multichannel AR coefficients of the two signals were calculated and then presented to the network to make a decision on tool wear. If the presented features are significantly different from previously learned patterns associated with a fresh tool, the network will recognize the difference and form a new category m worn tool. The experimental results show that tool wear can be effectively detected with or without minimum prior training using the self-organization property of the ART2 network.
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