Tool breakage detection in face milling by an unsupervised neural network

Tae Jo Ko; Hee Sul Kim; Dong Woo Cho

doi:10.1117/12.227216

21 November 1995 Tool breakage detection in face milling by an unsupervised neural network

Tae Jo Ko, Hee Sul Kim, Dong Woo Cho

Proceedings Volume 2596, Modeling, Simulation, and Control Technologies for Manufacturing; (1995) https://doi.org/10.1117/12.227216
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

This paper introduces a new tool breakage detection technique in face milling by using an unsupervised neural network. The cutting force signals are modeled by an autoregressive (AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on a 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 machining. ART 2 (Adaptive Resonance Theory 2) neural network is used for clustering of tool state using these parameters, and this network has a self organized capability without supervised learning. Therefore, this system operates successfully without a priori knowledge of the cutting process.

Citation Download Citation

Tae Jo Ko, Hee Sul Kim, and Dong Woo Cho "Tool breakage detection in face milling by an unsupervised neural network", Proc. SPIE 2596, Modeling, Simulation, and Control Technologies for Manufacturing, (21 November 1995); https://doi.org/10.1117/12.227216

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