Prediction of the Extrusion Load and Exit Temperature Using Artificial Neural Networks Based on FEM Simulation

Jia Zhou; Luo Xing Li; J. Mo; Jie Zhou; Jurek Duczczyk

doi:10.4028/www.scientific.net/KEM.424.241

Paper Titles

FEM-Assisted Design of a Multi-Hole Pocket Die to Extrude U-Shaped Aluminum Profiles with Different Wall Thicknesses
p.213

Localization of the Shear Zone in Extrusion Processes by Means of Finite Element Analysis
p.221

A Case Study to Solve the Problem of Wall Thickness Attenuation during Extrusion to Produce a Complex Hollow Magnesium Profile
p.227

Manufacturing of Carbon/Resin Separator by Die Sliding Extrusion
p.235

Prediction of the Extrusion Load and Exit Temperature Using Artificial Neural Networks Based on FEM Simulation
p.241

Contrasting Models to Determine the Approximate Extrusion Process Conditions for the First Billet
p.249

Study of Flow Balance and Temperature Evolution over Multiple Aluminum Extrusion Press Cycles with HyperXtrude 9.0
p.257

Numerical Design of Extrusion Process Using Finite Thermo-Elastoviscoplasticity with Damage. Prediction of Chevron Shaped Cracks
p.265

Integrated Extruder Plant Automation with Learning Control
p.273

HomeKey Engineering MaterialsKey Engineering Materials Vol. 424Prediction of the Extrusion Load and Exit...

Prediction of the Extrusion Load and Exit Temperature Using Artificial Neural Networks Based on FEM Simulation

Abstract:

In the present study, the extrusion process for the AZ31B magnesium alloy was simulated using a DEFORM-3D software package to establish a database in order to provide input data for artificial neural networks (ANN). The network model was trained by taking extrusion ratio, ram speed, shape complexity and ram displacement as the input variables and the extrusion load and exit temperature as the output parameters. The data from FEM simulations were submitted for ANN as a training file and then ANN built were used to predict the target parameters. The ANN predicted results were found to be in agreement with the FEM simulated and experimental measured ones.

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Periodical:

Key Engineering Materials (Volume 424)

Pages:

241-248

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.424.241

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Online since:

December 2009

Authors:

Jia Zhou, Luo Xing Li, J. Mo, Jie Zhou, Jurek Duczczyk

Keywords:

Artificial Neural Network (ANN), Extrusion, Finite Element (FE) Simulation, Magnesium

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