A Hierarchical Axiomatic Evaluation of Additive Manufacturing Equipment and the 3D Printing Process Based on Sustainability and Human Factors

Abstract

As interest in additive manufacturing (AM) continues to increase, it has become more important to have a robust method to help potential users select the AM process that best suits their technological needs while providing the greatest potential benefits in terms of sustainability and its effect on people. This paper presents the development of a framework for selecting the best AM process for a given application by considering both sustainability and human factors through the combination of axiomatic design and the analytic hierarchy process. Thirty-one participants with varying levels of expertise (novice and advanced users) were involved in the study, considering the frequency of 3D printer usage (novice users: never, rarely; expert users: sometimes, almost always, always) for prototyping parts. They employed fused deposition modeling (FDM) and stereolithography (SLA) (both 3D desktop printers) and collected data on five evaluation criteria. The participation of experts helped establish a novel methodology, with material cost deemed most important (49.8%), followed by cycle time (28%), energy consumption (11.7%), error rate (6.6%), and equipment noise (3.9%). The results showed that FDM was the optimal equipment option for advanced users. By examining the information content of the other options, it was found that FDM demanded less information than SLA, regardless of the user’s level of expertise. The proposed method is appropriate to assess the sustainability aspect of FDM and SLA; however, it can be further improved by adding indicators such as environmental impact, recyclability, and ergonomic and occupational health factors.