Title :
Classification of metal transfer mode using neural networks
Author :
Vincent, Daniel ; McCardle, John ; Stroud, Raymond
Author_Institution :
Neural Appl. Group, Brunel Univ., Egham, UK
Abstract :
To develop a control strategy for a metal inert gas (MIG) welding system it is necessary to classify several parameters in order to describe the process state. Neural networks have been identified as an appropriate processing technology because of the noisiness of weld data and the nonlinearity of the relationships between many of the process parameters. This paper describes the application of neural networks to the classification of metal transfer mode. We report on the analysis of the data, network selection, network development and evaluation of the final system
Keywords :
arc welding; manufacturing data processing; pattern classification; self-organising feature maps; Kohonen self organising feature map; MIG welding; metal inert gas welding; metal transfer mode classification; neural networks; process parameter; Appropriate technology; Control systems; Data analysis; Electrodes; Electromagnetic forces; Gas industry; Heat transfer; Metals industry; Neural networks; Spraying; Surface tension; Welding;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.488232
