Modeling Precipitate Dissolution in Hardened Aluminium Alloys using Neural Networks

R. López, B. Ducoeur, M. Chiumenti, B. Meester, C. Agelet de Saracibar

Abstract

This work presents a neural networks approach for finding the effective activation energy and modeling the dissolution rate of hardening precipitates in aluminium alloys using inverse analysis. As way of illustration, a class of multilayer perceptron extended with independent parameters is applied for that purpose to aluminium alloys AA-7449-T79, AA-2198-T8 and AA-6005A-T6.

How to cite

R. López, B. Ducoeur, M. Chiumenti, B. Meester and C. Agelet de Saracibar, Modeling Precipitate Dissolution in Hardened Aluminium Alloys using Neural Networks, Int. J. of Material Forming (2008). Vol. 1, pp. 1291-1294 URL https://www.scipedia.com/public/Draft_Samper_987028669

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