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Title of Journal: J Intell Manuf

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Abbravation: Journal of Intelligent Manufacturing

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Springer US

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DOI

10.1007/978-3-540-39903-2_73

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ISSN

1572-8145

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Modeling analysis and multiobjective optimizatio

Authors: Hamed Bakhtiari Mahdi Karimi Sina Rezazadeh
Publish Date: 2014/02/19
Volume: 27, Issue: 2, Pages: 463-473
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Abstract

Recently twist extrusion has found extensive applications as a novel method of severe plastic deformation for grain refining of materials In this paper two prominent predictive models response surface method and artificial neural network ANN are employed together with results of finite element simulation to model twist extrusion process Twist angle friction factor and ram speed are selected as input variables and imposed effective plastic strain strain homogeneity and maximum punch force are considered as output parameters Comparison between results shows that ANN outperforms response surface method in modeling twist extrusion process In addition statistical analysis of response surface shows that twist extrusion and friction factor have the most and ram speed has the least effect on output parameters at room temperature Also optimization of twist extrusion process was carried out by a combination of neural network model and multiobjective metaheuristic optimization algorithms For this reason three prominent multiobjective algorithms nondominated sorting genetic algorithm strength pareto evolutionary algorithm and multiobjective particle swarm optimization MOPSO were utilized Results showed that MOPSO algorithm has relative superiority over other algorithms to find the optimal points

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