Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling

Abstract Machine learning is proving to be an ideal tool for materials design, capable of predicting forward structure-property relationships, and inverse property-structure relationships. However, it has yet to be used extensively for materials engineering challenges, predicting post-processing/structure relationships, and has yet to be used for to predict structure/post-processing relationships for inverse engineering. This is often due to the lack of sufficient metadata, and the overall scarcity and imbalance of processing data in many domains. This topic is explored in the current study using binary and multi-class classification to predict the appropriate post-synthesis processing conditions for aluminium alloys, based entirely on the alloying composition. The data imbalance was addressed using a new guided oversampling strategy that improves model performance by simultaneously balancing the classes and avoiding noise that contributes to over-fitting. This is achieved by through the deliberate but strategic introduction of not-a-numbers (NaNs) and the use of algorithms that naturally avoid them during learning. The outcome is the successful training of highly accurate binary classifiers, with significant reductions in false negatives and/or false positives with respect to the classifiers trained on the original data alone. Superior results were obtained for models predicting whether alloys should be solutionised or aged, post-synthesis, by guiding the re-balancing of the classes based on features (metals) that are highly ranked by the classifier, and then doubling the size of the data set via interpolation. Overall, this strategy has the greatest impact on tasks with a Shannon Diversity Index greater than 1 or less than 0.5, but can be applied to any prediction of post-processing conditions as part of an inverse engineering workflow.