Title :
Model based fault detection in milling by classification of estimated cutting parameters
Author :
Konrad ; Isermann, H. ; Heintz, N.
Author_Institution :
Inst. of Autom. Control, Tech. Univ. of Darmstadt, Germany
Abstract :
This paper describes a new method of fault detection in milling. The presented detection algorithm is based on the estimation of particular model parameters using measured cutting force signals. By classifying the resulting patterns of the estimated parameters, the conditions of the cutting teeth can be determined
Keywords :
cutting; fault diagnosis; machine tools; machining; neural nets; parameter estimation; pattern classification; cutting; machining; milling; model based fault detection; neural networks; parameter estimation; pattern classification; Fault detection; Fault diagnosis; Feeds; Force measurement; Milling; Parameter estimation; Particle measurements; Signal generators; Signal processing; Teeth;
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
DOI :
10.1109/ICSMC.1995.538106
