Data envelopment analysis-based multi-criteria decision making for part orientation selection in fused deposition modeling

Abstract

Additive manufacturing (AM) has become popular in various fields, not only for industrial use, but also for personal use due to its key advantages in almost unlimited design freedom and material efficiency. Manufacturers in the industry recognize AM as a promising method in direct digital manufacturing for their design, research and development, and production processes. However, the applicability of AM technology is limited due to its process instability from several factors including the orientation selection of the part. Orientation of a part refers to the building direction with respect to the part being fabricated by the AM machine. In addition, available quantitative methods to determine the part orientation are limited. In this paper, we examine the part orientation alternatives' efficiency using data envelopment analysis (DEA). We illustrate a case study for one AM process; fused deposition modeling (FDM). The orientation alternatives' efficiency is identified and presented through trade-offs among conflicting criteria and machines. By using the DEA analysis, it provides insights regarding efficiency of each alternative, which can be used for the benchmarking leading to a best-practice frontier. The proposed method can be applied to other AM technologies in the industrial AM-based production environments for effective management.