Controlling 1000 amps using neural networks

Author

Shroud, R.R. ; Swallow, S. ; McCardle, J.R. ; Burge, K.T.

Author_Institution

Dept. of Ind. Design, Brunel Univ., Uxbridge, UK

Volume

2

fYear

1993

fDate

25-29 Oct. 1993

Firstpage

1857

Abstract

The continued effort to improve working conditions and efficiency in fusion welding has increased automation and taken the operator further from the workpiece. This inherently has increased the demand for improved monitoring and control systems to cope with the increase in throughput. The paper describes an application of two network architectures to control submerged arc welding-a high current, low voltage automatic joining process. A logical discriminator, implemented in hardware is used to identify time/amplitude return echoes derived from ultrasonic interrogation of the arc vicinity and a Kohonen feature map is used to classify arc sound.

Keywords

acoustic signal processing; arc welding; computerised monitoring; echo; electric current control; process control; self-organising feature maps; 200 to 1000 A; Kohonen feature map; arc sound classification; automatic joining process; control systems; electric current control; fusion welding; logical discriminator; monitoring; network architectures; neural networks; submerged arc welding; time/amplitude return echoes; ultrasonic interrogation; Automatic control; Automatic voltage control; Automation; Control systems; Employee welfare; Joining processes; Low voltage; Neural networks; Throughput; Welding;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on

Print_ISBN

0-7803-1421-2

Type

conf

DOI

10.1109/IJCNN.1993.717017

Filename

717017