A decision support system for the validation of metal powder bed-based additive manufacturing applications

The purpose of this research is to develop a computer-driven decision support system (DSS) to select optimal additive manufacturing (AM) machines for metal powder bed fusion (PBF) applications. The tool permits to evaluate productivity factors (i.e., cost and production time) for any given geometry. At the same time, the trade-off between feature resolution and productivity analysis is visualized and a sensitivity analysis is performed to evaluate future cost developments. This research encompasses a decision support system that includes a data structure and an algorithm which is coded in “MathWorks Matlab,” considering cost structures for metal-based AM (i.e., machine cost, material cost, and labor cost). Results of this research demonstrate that feature resolution has a crucial effect on the total cost per part, but displays decreasing impacts for higher build volume rates. Based on assumptions of business consultancies, productivity can be increased, resulting in a potential decline of cost per part of up to 55% until 2025. Using this DSS tool, it is possible to evaluate the most optimal AM production systems by selecting between several input parameters. The algorithm allows industry practitioners to retrieve information and assist in decision-making processes, including cost per part, total cost comparison, and build time evaluations for typical commercial metal PBF systems.