Flow curve prediction of ZAM100 magnesium alloy sheets using artificial neural network-based models

El Mehtedi, Mohamad
First
;
2019-01-01

Abstract

A multivariable empirical model, based on an artificial neural network (ANN), was developed to predict flow curves of ZAM100 magnesium alloy sheets as a function of process parameters in hot forming conditions. Tensile tests were performed in a wide range of temperature and strain rate to collect the dataset used in the training and testing stages of the network. The generalization ability of the model was tested using both the leave-one-out cross-validation method and flow curves not belonging to the training set. The excellent fitting between experimental and predicted curves was proven the very good predictive capability of the model.
2019
Inglese
Procedia CIRP
Elsevier B.V.
Napoli
ITALIA
Roberto Teti, Doriana M. D'Addona
79
661
666
6
http://www.sciencedirect.com/science/journal/22128271
12th CIRP Conference on Intelligent Computation in Manufacturing Engineering
Contributo
Esperti anonimi
18-20 July 2018
Naples, Italy
internazionale
scientifica
Artificial neural network; Flow curve; Magnesium alloy; Control and Systems Engineering; Industrial and Manufacturing Engineering
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
El Mehtedi, Mohamad; Forcellese, Archimede; Greco, Luciano; Pieralisi, Massimiliano; Simoncini, Michela
273
5
4.1 Contributo in Atti di convegno
open
info:eu-repo/semantics/conferencePaper
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