Inverse design of aluminium alloys using multi-targeted regression

Abstract

The 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.

Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow

“…Machine learning has emerged as a powerful tool for identifying non-linear relationships in metallic alloys (including Al alloys), successfully predicting mechanical properties based on alloy compositions and processing conditions [10][11][12][13][14][15][16]. Random forest models have predicted tensile strength and elongation in wrought Al alloys with 11% and 14% error rates, respectively [15].…”