Abstract
This paper applies an integrated approach using the Taguchi method, neural network (NN) and genetic algorithm (GA) to optimize the tensile-shear strength of resistance spot welding (RSW) specimens in automotive industry. The proposed approach consists of two stages. First stage executes initial optimization via Taguchi method to construct a database for the NN. In second stage, a NN with Levenberg-Marquardt back-propagation (LMBP) algorithm is used to provide the nonlinear relationship between factors and the response. Then, a GA is applied to obtain the optimal factor settings. The experimental results showed that the tensile-shear strength of the optimal welding parameter via the proposed approach is better than apply Taguchi method only.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
