Additive Manufacturing Service Provider Selection Using a Neutrosophic Best Worst Method

Abstract

Additive manufacturing (AM) is poised to disrupt the manufacturing industry due to its distinctive (layer-by-layer) approach to part production, as AM comprises a range of fabrication techniques that facilitate the production of customized and highly complex parts. For a particular part selecting the most suitable AM machine and material is an indispensable decision due to several AM machines, their process and material constraints like size, accuracy, mechanical properties etc. This paper demonstrates the multi-criteria framework using Delphi and neutrosophic best-worst method to determine the best suitable AM machine and feasible material from the spool of databases. The Delphi method is employed to determine and validate essential criteria for a selected part, which results in shortlisting the 9 most relevant criteria. Further, the neutrosophic best-worst method is utilized to compute the criteria weights. A rating and normalization approach is deployed to calculate each AM machine and compatible material aggregate score to determine the optimal AM machine, material and ultimately, the AM service provider.