Optimizing the RSW Process Quality of Auto-Body via the Taguchi Method and a Neural-Genetic Approach

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 97-101)

Edited by:

Zhengyi Jiang and Chunliang Zhang

Pages:

3899-3904

DOI:

10.4028/www.scientific.net/AMR.97-101.3899

Citation:

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

Online since:

March 2010

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Price:

$35.00

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