Tool Wear Monitoring Using Radial Basis Function Neural Network (CROSBI ID 498700)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Brezak, Danko ; Udiljak, Toma ; Mihoci, Kristijan ; Majetić, Dubravko ; Novaković, Branko ; Kasać, Josip
engleski
Tool Wear Monitoring Using Radial Basis Function Neural Network
This paper considers the application of Radial Basis Function neural network (RBFNN) for tool wear determination in the milling process. Tool wear, i.e. flank wear zone widths, have been estimated in two phases using two types of RBFNN algorithms. In the first phase, RBFNN pattern recognition algorithm is used in order to classify tool wear features in three wear level classes (initial, normal and rapid tool wear). On behalf of these results, in the second phase, RBFNN regression algorithm is utilized to estimate the average amount of flank wear zone widths. Tool wear features were extracted in time and frequency domain from three different types of signals: force, acoustic emission and nominal currents of feed drives.
Tool wear monitoring; Radial Basis Function Neural Network; Milling
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
1859-1863-x.
2004.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of International Joint Conference on Neural Networks - IJCNN 2004
Szolgay, Péter
Budimpešta: Institute of Electrical and Electronics Engineers (IEEE)
Podaci o skupu
IJCNN 2004 - International Joint Conference on Neural Networks
predavanje
25.07.2004-29.07.2004
Budimpešta, Mađarska