• Birbilis, Nick
• Bhat, Ninad

Abstract

<p>The widespread use of aluminium alloys in the aerospace, transport and marine industries is attributed to their desirable physical properties. The relationship between the alloy composition and microstructure and the resultant mechanical properties is complicated. Machine learning (ML) has become a valuable asset in designing new alloys. The accuracy of previously utilised ML models has been increased by partitioning the alloy data set and training regressors for individual partitions. This study uses a recently reported data-driven partitioning scheme that divides the data into classes based on feature similarity. Individual regressors were trained on each class and compared with the regressor trained on the entire data set. It was revealed that individual class-based regressors are more interpretable without loss in prediction accuracy. The results indicate that the data-driven partitioning scheme outperforms traditional domain knowledge based partitioning, providing both increased model accuracy and increased model interpretability.</p>

Topics

• impedance spectroscopy
• microstructure
• aluminium
• aluminium alloy
• machine learning
• alloy composition