  Autonomous Experimentation Platforms (AEPs) are advanced manufacturing
platforms that, under intelligent control, can sequentially search the material
design space (MDS) and identify parameters with the desired properties. At the
heart of the intelligent control of these AEPs is the policy guiding the
sequential experiments, which is to choose the location to carry out the next
experiment. In such cases, a balance between exploitation and exploration must
be achieved. A Bayesian Optimization (BO) framework with Expected Improvement
based (EI-based) acquisition function can effectively search the MDS and guide
where to conduct the next experiments so that the underlying relationship can
be identified with a smaller number of experiments. The traditional BO
framework tries to optimize a black box objective function in a sequential
manner by relying on a single model. However, this single-model approach does
not account for model uncertainty. Bayesian Model Averaging (BMA) addresses
this issue by working with multiple models and thus considering the uncertainty
in the models. In this work, we first apply the conventional BO algorithm with
the most popular EI-based experiment policy in a real-life fatigue dataset for
steel to predict the fatigue strength of steel. Afterward, we apply BMA to the
same dataset by working with a set of predictive models and compare the
performance of BMA with the traditional BO algorithm, which relies on a single
model for approximation. We compare the results in terms of RMSE and find that
BMA performs better than EI-based BO in the prediction task by considering the
model uncertainty in its framework.
