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2013 (Vol. 6, Issue: 13)
Article Information:

Application of ANOVA and Taguchi-based Mutation Particle Swarm Algorithm for Parameters Design of Multi-hole Extrusion Process

Wen-Jong Chen, Wen-Cheng Su, Fung-Ling Nian, Jia-Ru Lin and Dyi-Cheng Chen
Corresponding Author:  Wen-Jong Chen 

Key words:  F-value, multi-hole extrusion, signal-to-noise, Taguchi-based MPSO, , ,
Vol. 6 , (13): 2316-2325
Submitted Accepted Published
November 13, 2012 January 01, 2013 August 05, 2013
Abstract:

This study presents the Taguchi method and the Particle Swarm Optimization (PSO) technique which uses mutation (MPSO) and dynamic inertia weight to determine the best ranges of process parameters (extrusion velocity, eccentricity ratio, billet temperature and friction coefficient at the die interface) for a multi-hole extrusion process. A L18(2137) array, signal-to-noise (S/N) ratios and analysis of variance (ANOVA) at 99% confidence level were used to indicate the optimum levels and the effect of the process parameters with consideration of mandrel eccentricity angle and exit tube bending angle. As per the Taguchi-based MPSO algorithm using DEFORMTM 3D Finite Element Analysis (FEA) software, the minimum mandrel eccentricity and exit tube bending angles were respectively calculated to be 0.03, which are significantly less than those based on Genetic Algorithm (GA) and the Taguchi method, respectively. This indicates that the Taguchi-based MPSO algorithm can effectively and remarkably reduce the warp angles of Ti-6Al-4V extruded products and the billet temperature is the most influencing parameter. The results of this study can be extended to multi-hole extrusion beyond four holes and employed as a predictive tool to forecast the optimal parameters of the multi-hole extrusion process.
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  Cite this Reference:
Wen-Jong Chen, Wen-Cheng Su, Fung-Ling Nian, Jia-Ru Lin and Dyi-Cheng Chen, 2013. Application of ANOVA and Taguchi-based Mutation Particle Swarm Algorithm for Parameters Design of Multi-hole Extrusion Process.  Research Journal of Applied Sciences, Engineering and Technology, 6(13): 2316-2325.
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ISSN (Online):  2040-7467
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