#5725. Numerical modeling and optimization of process parameters in continuous extrusion process through response surface methodology, artificial neural network & genetic algorithm

August 2026publication date
Proposal available till 29-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Applied Mathematics;
Computational Mathematics;
Numerical Analysis;
Engineering (all);
Theoretical Computer Science;
Computational Theory and Mathematics;
Software;
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Abstract:
The product of high complex profile, high strength, high productivity and excellent material properties with infinite length can be produced by Continuous Extrusion (CE) process. The numerical simulation of Aluminum (AA 1100) feedstock material at different wheel velocities, product diameter, feedstock temperature, die temperature and friction condition has been carried out using 3D simulation tool Design Environment for Forming (DEFORM-3D) in this paper. The development of mathematical model is carried out to investigate the influence of wheel velocity, extrusion ratio, feedstock temperature, die temperature and friction conditions on total load required for the deformation and extrusion of feedstock material through Response Surface Methodology (RSM). The statistical significance of mathematical model is verified through analysis of variance (ANOVA).
Keywords:
AA1100; artificial neural network; Continuous extrusion; DEFORM-3D; genetic algorithm; response surface methodology

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