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.

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Published on 25/07/19

DOI: 10.1007/s12289-008-0139-4
Licence: CC BY-NC-SA license

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