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Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results

Author

Listed:
  • Hamed Bakhtiari

    (Bu-Ali Sina University)

  • Mahdi Karimi

    (Bu-Ali Sina University)

  • Sina Rezazadeh

    (Islamic Azad University, Qazvin Branch)

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 multi-objective meta-heuristic optimization algorithms. For this reason, three prominent multi-objective algorithms, non-dominated sorting genetic algorithm, strength pareto evolutionary algorithm and multi-objective particle swarm optimization (MOPSO) were utilized. Results showed that MOPSO algorithm has relative superiority over other algorithms to find the optimal points.

Suggested Citation

  • Hamed Bakhtiari & Mahdi Karimi & Sina Rezazadeh, 2016. "Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 463-473, April.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:2:d:10.1007_s10845-014-0879-6
    DOI: 10.1007/s10845-014-0879-6
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    Cited by:

    1. Wurong Fu, 2021. "Macroscopic numerical model of reinforced concrete shear walls based on material properties," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1401-1410, June.
    2. Alejandro Alvarado-Iniesta & Luis Gonzalo Guillen-Anaya & Luis Alberto Rodríguez-Picón & Raul Ñeco-Caberta, 2020. "Multi-objective optimization of an engine mount design by means of memetic genetic programming and a local exploration approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 19-32, January.
    3. Pauline Ong & Choon Sin Ho & Desmond Daniel Vui Sheng Chin & Chee Kiong Sia & Chuan Huat Ng & Md Saidin Wahab & Abduladim Salem Bala, 2019. "Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1957-1972, April.

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