Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing
This work is incremental, offering a review and guide for experimental-design practitioners in fields like materials science and additive manufacturing.
The authors tackled the problem of applying Bayesian optimization (BO) for sequential experimental design, particularly in additive manufacturing, by providing a comprehensive manual covering methodology, software comparisons, and potential new applications, resulting in a resource that highlights current trends and facilitates broader use of BO in experimental settings.
Bayesian optimization (BO) is an approach to globally optimizing black-box objective functions that are expensive to evaluate. BO-powered experimental design has found wide application in materials science, chemistry, experimental physics, drug development, etc. This work aims to bring attention to the benefits of applying BO in designing experiments and to provide a BO manual, covering both methodology and software, for the convenience of anyone who wants to apply or learn BO. In particular, we briefly explain the BO technique, review all the applications of BO in additive manufacturing, compare and exemplify the features of different open BO libraries, unlock new potential applications of BO to other types of data (e.g., preferential output). This article is aimed at readers with some understanding of Bayesian methods, but not necessarily with knowledge of additive manufacturing; the software performance overview and implementation instructions are instrumental for any experimental-design practitioner. Moreover, our review in the field of additive manufacturing highlights the current knowledge and technological trends of BO. This article has a supplementary material online.