Optimizing the RSW Process Quality of Auto-Body via the Taguchi Method and a Neural-Genetic Approach
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.
Zhengyi Jiang and Chunliang Zhang
H. L. Lin and C. P. Chou, "Optimizing the RSW Process Quality of Auto-Body via the Taguchi Method and a Neural-Genetic Approach", Advanced Materials Research, Vols. 97-101, pp. 3899-3904, 2010