Mastering manufacturing: exploring the influence of engineering designers’ prior experience when using design for additive manufacturing

Abstract

Additive manufacturing (AM) presents designers with unique manufacturing capabilities while imposing several limitations. Designers must leverage AM capabilities – through opportunistic design for AM (O-DfAM) – and accommodate AM limitations – through restrictive (R-) DfAM – to successfully employ AM in engineering design. This dual DfAM approach – comprising O- and R-DfAM – starkly contrasts traditional, limitation-based design for manufacturing (DfM). Therefore, designers must transition from a restrictive DfM mindset towards a ‘dual’ design mindset. Designers’ prior experience, especially with DfM could inhibit their ability to transition towards dual DfAM. On the other hand, experienced designers’ auxiliary skills (e.g. with computer-aided design) could help them implement DfAM in their solutions. However, little research has studied the influence of prior experience on DfAM use in the later design stages (i.e. embodiment and detail design), and we explore this research gap. Specifically, we conducted an experimental study comprising a task-based DfAM educational intervention with first-year student designers and upper-level student designers. Participants’ DfAM self-efficacy and their integration of DfAM in their solutions were compared between the two groups. From our results, we see that experienced designers report higher baseline self-efficacy with R-DfAM but not O-DfAM. We also see that experienced designers demonstrate a greater use of certain DfAM concepts (e.g. part and assembly complexity) in their designs. These findings suggest that introducing designers to O-DfAM early could help develop a dual design mindset; however, having more engineering experience might be necessary for them to implement DfAM into their designs.

KEYWORDS:

• Prior experience
• design education
• design for additive manufacturing

Acknowledgements

This research was funded by the National Science Foundation under grant number CMMI-1712234. Any opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF. We would also like to thank Dr. Stephanie Cutler for their help in planning and executing this research. An earlier version of this research was presented at the 2021 ASME Design Automation Conference at the International Design Engineering Technical Conferences (Prabhu et al. 2021c).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Science Foundation [Grant No. CMMI-1712234].