Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing

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.

An Evaluation of Classification Methods for 3D Printing Time-Series Data

Additive Manufacturing presents a great application area for Machine Learning because of the vast volume of data generated and the potential to mine this data to control outcomes. In this paper we present preliminary work on classifying infrared time-series data representing melt-pool temperature in a metal 3D printing process. Our ultimate objective is to use this data to predict process outcomes (e.g. hardness, porosity, surface roughness). In the work presented here we simply show that there is a signal in this data that can be used for the classification of different components and stages of the AM process. In line with other Machine Learning research on time-series classification we use k-Nearest Neighbour classifiers. The results we present suggests that Dynamic Time Warping is an effective distance measure compared with alternatives for 3D printing data of this type.