Modelling and optimization of the resistance spot welding process via a Taguchi—neural approach in the automobile industry

Abstract Many parameters affect the quality of the resistance spot welding (RSW) process. It is not easy to obtain optimal parameters of the RSW process in the automobile industry. Conventionally, the Taguchi method has been widely used in engineering; however, with this method the desired results can only be obtained with the use of very discrete control factors, thus leading to uncertainty about the real optimum. In the process to weld the low-carbon sheet steels of the auto body, the Taguchi method was used for the initial optimization of the RSW process parameters. A neural network with the Levenberg—Marquardt back-propagation algorithm was then adopted to develop the relationships between the welding process parameters and tensile shear strength of each specimen. The optimal parameters of the RSW process were determined by simulating the process parameters using a well-trained neural network model. Experimental results illustrate the Taguchi—neural approach.

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