An Automated Data Collection System to Capture the Co-evolution of Physical Prototypes and Designer Knowledge
Open Access
- Author:
- Nelson, Jacob
- Graduate Program:
- Mechanical Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- March 24, 2020
- Committee Members:
- Jessica Dolores Menold, Thesis Advisor/Co-Advisor
Christopher Carson Mccomb, Committee Member
Jessica Dolores Menold, Committee Member
Karen Ann Thole, Program Head/Chair - Keywords:
- prototyping
engineering design
design data collection
prototypes - Abstract:
- Prototyping is a crucial activity throughout the design process, but its effects on designers and the designs they create remain understudied. This is, in part, due to limitations in how researchers studying engineering design are able to collect data on prototyping in complex, non-linear projects occurring over extended timelines. Data collection in authentic design settings imposes a significant burden on the researcher, requiring large investments of time and money to collect meaningful samples. Further, in authentic design settings it can be difficult to construct controlled experiments and collect quantifiable data throughout the design project, which results in researchers relying on large volumes of qualitative data from methods such as interviews. In this work we propose a new system called ARCHIE (Archiving Results and Capturing Human Innovation in Engineering) to aid researchers in data collection, by recording physical prototypes and their associated designer knowledge at multiple points during the design process. This work provides results from two studies utilizing ARCHIE in two semester-long student design projects. In both projects, ARCHIE was used to capture data on physical prototyping efforts, investigate how students used prototyping throughout the project. Data was collected exploring what they learned from prototyping, and how this influenced their perception of the value of their prototypes. The relationships between time, cost, and prototyping tendencies were also explored. The results of this work reinforce descriptions of early stage prototyping from prior work, while highlighting the need for new metrics to quantify the value of a prototyping effort.