Application of Experimental Design-Based Predictive Models and Optimization in Additive Manufacturing – a Review

Abstract

Experiments play a crucial role in additive manufacturing to help researchers develop new materials with enhanced properties or in several types of process optimization tasks. Design of Experiments (DoE) is a valuable tool that is efficient, statistically rigorous and offers a systematic approach to experimentation. In this article, several types of DoE methods such as one-factor-at-a-time (OFAT), full and fractional factorial designs, Taguchi, response surface methodology (RSM) and descriptive screening designs (DSD) are briefly described in addition to some single- and multi-objective optimization methods. The optimization methods apply utility theory (UT), Taguchi and desirability optimization as well as some non-conventional, artificial intelligence-based multi-objective optimization methods illustrated by examples from the field of additive manufacturing. In the second part, the potential factors and response variables are reviewed during the investigation of the seven main categories of additive manufacturing, namely binder jetting (BJT), directed energy deposition (DED), material extrusion (MEX), material jetting (MJT), powder bed fusion (PBF), sheet lamination (SHL) and vat photopolymerization (VPP).