Characterizing the effects of additive manufacturing process settings on part performance using approximation-assisted multi-objective optimization

Additive manufacturing (AM) systems present tremendous advantages over the traditional manufacturing systems, particularly for realizing complex shapes and producing custom parts. These advantages, and the rapid growth in the availability of AM systems, have led to a widespread increase in the use of AM. Unfortunately, general design for additive manufacturing (DfAM) tools that predict the engineering performance of AM parts are largely unavailable, and this has forced engineers to rely on engineering judgment and/or data from physical experiments. The data sets reported in the literature that quantify the performance of the existing AM systems provide much needed insight, but the data produced by experiments conducted on specific AM systems are necessarily of limited applicability. Given the large number of AM systems currently in use today, a potentially vast amount of data are needed to fully understand them all, and new AM systems and materials are being developed almost daily. To mitigate this need for an ever-increasing amount of data, this paper proposes the use of an approximation-assisted multi-objective optimization technique to quantify the performance of an AM system for specific design application in a targeted fashion. This general approach uses a limited number of physical experiments to answer a specific DfAM question of interest, and the results can then be used to design functional parts without the need for additional data. The implementation of the proposed approach is demonstrated with a material extrusion case study.