Classification of DC micro spot welding quality using fuzzy ARTMAP on acoustic emission monitoring

Author

Prateepasen, Asa ; Kaewtrakulpong, Pakorn ; Jirarungsatean, Chalermkiat

Author_Institution

Fac. of Eng., King Mongkut´´s Inst. of Technol., Bangkok, Thailand

Volume

D

fYear

2004

fDate

21-24 Nov. 2004

Firstpage

649

Abstract

This paper presents a fuzzy ARTMAP to classify quality of nugget formation in DC micro spot welding process using online extraction of AE parameters. The fuzzy ARTMAP is proposed to classify the quality of nugget formation into one of three levels: "weak", "good", and "excessive". It is chosen over feedforward neural networks due to its appealing properties. These include automatic selection of network structure, convergence properties and its online learning. An experiment was conducted to show its performance. Peel and metallographic tests plus spatter exploding observation were used to identify the quality of nugget formation. The result shows that the approach performs well. In addition, the performance can be enhanced greatly if spatter exploding observation is used in combination with the AE parameters.

Keywords

acoustic emission; convergence; feedforward neural nets; fuzzy systems; learning (artificial intelligence); metallography; power engineering computing; spot welding; AE; DC micro spot welding; acoustic emission monitoring; convergence property; feedforward neural network; fuzzy ARTMAP; metallographic test; nugget formation; online learning; peel test; spatter exploding observation; Acoustic emission; Monitoring; Spot welding;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2004. 2004 IEEE Region 10 Conference

Print_ISBN

0-7803-8560-8

Type

conf

DOI

10.1109/TENCON.2004.1415016

Filename

1415016