Share Email Print

Proceedings Paper

Tool breakage detection in face milling by an unsupervised neural network
Author(s): Tae Jo Ko; Hee Sul Kim; Dong Woo Cho
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 21 November 1995
PDF: 12 pages
Proc. SPIE 2596, Modeling, Simulation, and Control Technologies for Manufacturing, (21 November 1995); doi: 10.1117/12.227216
Show Author Affiliations
Tae Jo Ko, Yeungnam Univ. (South Korea)
Hee Sul Kim, Yeungnam Univ. (South Korea)
Dong Woo Cho, Pohang Univ. of Science and Technology (South Korea)

Published in SPIE Proceedings Vol. 2596:
Modeling, Simulation, and Control Technologies for Manufacturing
Ronald Lumia, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?