Transactions of the Korean Society of Mechanical Engineers (대한기계학회논문집)
- Volume 18 Issue 8
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- Pages.1939-1951
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- 1994
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- 1225-5963(pISSN)
DOI QR Code
Tool Breakage Detection in Face Milling Using a Self Organized Neural Network
자기구성 신경회로망을 이용한 면삭밀링에서의 공구파단검출
Abstract
This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.
Keywords