Resistance Spot Welding Optimization Based on Artificial Neural Network. (9th November 2014)

• Record Type: Journal Article
• Authors: Arunchai, Thongchai; Sonthipermpoon, Kawin; Apichayakul, Phisut; Tamee, Kreangsak
• Other Names: Chen Quanfang Academic Editor.
• Is Part Of: International journal of manufacturing engineering. Volume 2014(2014)
• Publication Date: 2014-11-09
• Subjects: Production engineering -- Periodicals; Production engineering; Periodicals; 670.5
• DOI: 10.1155/2014/154784
• Languages: English
• ISSNs: 2356-7023

Abstract

Abstract : Resistance Spot Welding (RSW) is processed by using aluminum alloy used in the automotive industry. The difficulty of RSW parameter setting leads to inconsistent quality between welds. The important RSW parameters are the welding current, electrode force, and welding time. An additional RSW parameter, that is, the electrical resistance of the aluminum alloy, which varies depending on the thickness of the material, is considered to be a necessary parameter. The parameters applied to the RSW process, with aluminum alloy, are sensitive to exact measurement. Parameter prediction by the use of an artificial neural network (ANN) as a tool in finding the parameter optimization was investigated. The ANN was designed and tested for predictive weld quality by using the input and output data in parameters and tensile shear strength of the aluminum alloy, respectively. The results of the tensile shear strength testing and the estimated parameter optimization are applied to the RSW process. The achieved results of the tensile shear strength output were mean squared error (MSE) and accuracy equal to 0.054 and 95%, respectively. This indicates that that the application of the ANN in welding machine control is highly successful in setting the welding parameters.