Modeling Precipitate Dissolution in Hardened Aluminium Alloys using Neural Networks

Autor:	B. Ducoeur, R. Lopez, C. Agelet de Saracibar, B. de Meester, Michele Chiumenti
Rok vydání:	2008
Předmět:	Engineering Civil Materials science Artificial neural network Metallurgy chemistry.chemical_element Activation energy Engineering Marine Engineering Mechanical chemistry Aluminium Multilayer perceptron Engineering Industrial Hardening (metallurgy) General Materials Science Inverse analysis Dissolution
Zdroj:	Scipedia Open Access Scipedia SL
ISSN:	1960-6214 1960-6206
DOI:	10.1007/s12289-008-0139-4
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bea8afddace18af712ef73a3bc72a730 https://doi.org/10.1007/s12289-008-0139-4 Zobrazit plný text záznamu Full text from SpringerLink