Assessment of Bayesian guidance strategy to develop bake-hardening ferritic steel

Developing alloys with optimal properties involves tuning several compositional and processing parameters. As the parametric space is often high-dimensional, doing so may require a prohibitively large number of experiments, which demand ample time and physical resources. In this study, we examine whether the method of Bayesian optimisation, which involves a sequential function evaluation scheme, applies to designing metallic alloys. We consider the example of bake-hardening ferritic steel and aim to maximise the extent of bake-hardening by tuning more than a dozen parameters while reducing the number of experimental fabrications and measurements. To this end, an existing dataset has been used to create a regression model, which acts as a surrogate for experimental measurements. The Bayesian optimiser has been implemented with three different covariance functions and two acquisition functions. Our results suggest that a design strategy guided by Bayesian optimisation has the potential to substantially reduce the time and cost of developing alloys through experimental routes.