SVM-based fuzzy rules acquisition system for pulsed GTAW process

Authors:
Xixia Huang;Fanhuai Shi;Wei Gu;Shanben Chen
Affiliations:
Marine Technology & Control Engineering Key Laboratory, Shanghai Maritime University, 1550 Pudong Avenue, Shanghai 200135, China;Welding Engineering Institute, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Marine Technology & Control Engineering Key Laboratory, Shanghai Maritime University, 1550 Pudong Avenue, Shanghai 200135, China;Welding Engineering Institute, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

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Abstract

This paper proposes a support vector machine-based fuzzy rules acquisition system (SVM-FRAS) for modeling of the gas tungsten arc welding (GTAW) process. The character of SVM in extracting support vector provides a mechanism to extract fuzzy IF-THEN rules from the training data set. We construct the fuzzy inference system using fuzzy basis function. The gradient technique is used to tune the fuzzy rules and the inference system. Theoretical analysis and comparative tests are performed comparing with other fuzzy systems. Modeling is one of the key techniques in the automatic control of the arc welding process, and is still a very difficult problem. Comprehensibility is one of the required characteristics in modeling for the complex GTAW process. We use the proposed SVM-FRAS to obtain the rule-based model of the aluminum alloy pulse GTAW process. Experimental results show the SVM-FRAS model possesses good generalization capability as well as high comprehensibility.