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

Hsuan Liang Lin; Chang Pin Chou

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

Paper Titles

Analysis on Microstructure of Laser Brazing Diamond Grits with a Ni-Based Filler Alloy
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Pitting Corrosion Behavior in Laser Weld Metals of 2205 DSS with Continuous Heat Treatment
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Interfacial Microstructure and Phase Constitution of the Cu/Al Dissimilar Metals Joint Made by Contact Reactive Brazing
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Linear Friction and Electron Beam Welded Joints of Ti₂AlNb/TC11
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Optimizing the RSW Process Quality of Auto-Body via the Taguchi Method and a Neural-Genetic Approach
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Embedment of HA into Superplastic Ti-6Al-4V: Effects of Implantation Temperature
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Mechanical Properties of Single-Walled Carbon Nanotubes under Large Axial Deformation
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Study on Joint Residual Stress Induced by Laser Beam Welding of High Strength Automobile Thin Sheet
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Bond Thickness Effects upon Dynamic Behaviour in Adhesive Joints
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 97-101Optimizing the RSW Process Quality of Auto-Body...

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

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.

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Periodical:

Advanced Materials Research (Volumes 97-101)

Pages:

3899-3904

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.97-101.3899

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Online since:

March 2010

Authors:

Hsuan Liang Lin, Chang Pin Chou

Keywords:

Genetic Algorithm (GA), Neural Network (NN), Resistance Spot Welding (RSW), Taguchi Method

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