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Inverse design of aluminium alloys using multi-targeted regression

Version 2 2024-06-03, 02:48
Version 1 2024-02-07, 04:46
journal contribution
posted on 2024-06-03, 02:48 authored by N Bhat, AS Barnard, Nick BirbilisNick Birbilis
AbstractThe traditional design process for aluminium alloys has primarily relied upon iterative alloy production and testing, which can be time intensive and expensive. Machine learning has recently been demonstrated to have promise in predicting alloy properties based on the inputs of alloy composition and alloy processing conditions. In the search for optimal alloy concentrations that meet desired properties, as the search space expands, the optimisation process can become more time intensive and computationally expensive, depending on the methodology used. We propose a faster workflow for inverse alloy design by using multi-target machine-learning models. We train a random forest regressor to predict the concentration of alloying elements and a random forest classifier to determine the processing condition. We further analysed the inverse model and validated findings against alloys reported in the literature.

History

Journal

Journal of Materials Science

Volume

59

Pagination

1448-1463

Location

Berlin, Germany

ISSN

0022-2461

eISSN

1573-4803

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

4

Publisher

Springer