Optimizing the user experience of additive manufacturing products through material driven design

Abstract

Material selection in additive manufacturing (AM) is typically driven by the need to meet specific functional characteristics, often resulting in objects with limited user experiential interpretation. With the advent of new materials and recycling tech- niques in AM, it is crucial to understand the sensory and emotional responses these materials can evoke. While the literature extensively covers the mechanical properties of 3D printed objects, a notable gap exists in studies focusing on user interaction and perception. This research addresses this gap by applying the material driven design (MDD) methodology to a case study that explores a new mixture of waste material from a polymer bed powder fusion process. To ensure a reliable and systematic approach, we integrated statistical tools such as Goodness of Fit, Pairwise comparison, ANOVA, and interval graphs into the MDD. We conducted a comprehensive study involving 42 participants, using a vocabulary of 65 descriptors from the Ma2E4 toolkit for experiential characterization. The study’s findings resulted in a proposed process that made it possible to categorize and rank products into eight distinct families, identifying 36 potential objects. One of these objects was selected and, following a design process, fabricated using binder jetting technology. This research emphasizes the importance of incorporating user experience in AM and highlights the potential of sustainable waste utilization to advance the field. By focusing on functional and experiential aspects, we can develop 3D-printed items that enhance performance and engage users more effectively.