  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.
