Title :
Selection of optimum friction welding condition using neural networks
Author :
Ogawa, Koichi ; Yamaguchi, Hiroshi ; Yamamoto, Yoshiaki ; Kurozawa, Toshiro
Author_Institution :
Osaka Prefecture Univ., Japan
Abstract :
A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction welding condition, burn-off length and joint strength on 5056 aluminum alloy friction welding. The learning of the synapse weights of the neural network is performed using an extended Kalman filtering algorithm. The results of analysis suggest that the proposed method is an effective method to select an optimum welding condition.
Keywords :
Kalman filters; aluminium alloys; filtering theory; friction; learning (artificial intelligence); neural nets; nonlinear filters; welding; 5056 aluminum alloy; Kalman-neuro algorithm; burn-off length; extended Kalman filtering algorithm; joint strength; neural networks; optimum friction welding condition; synapse weights; Aluminum alloys; Artificial neural networks; Data analysis; Filtering algorithms; Friction; Logic; Neural networks; Process control; Shape; Welding;
Conference_Titel :
SICE 2002. Proceedings of the 41st SICE Annual Conference
Print_ISBN :
0-7803-7631-5
DOI :
10.1109/SICE.2002.1195759
