Comparing Fabrication Workflows in CAD to Support Design Reasoning

Shuo Feng
Information Science
Cornell Tech
NYC, New York, USA
sf522@cornell.edu
Xuening Wang
Information Science
Cornell Tech
NYC, New York, USA
xw672@cornell.edu
Yifan Shan
Information Science
Cornell Tech
NYC, New York, USA
ys2253@cornell.edu
Krista U Singh
Macaulay Honors College
NYC, New York, USA
krista.singh@macaulay.cuny.edu
Bo Liu
Computer Science
Cornell Tech
NYC, New York, USA
bl685@cornell.edu
Amritansh Kwatra
Information Science
Cornell Tech
NYC, New York, USA
ak2244@cornell.edu
Ritik Batra
Information Science
Cornell Tech
NYC, New York, USA
rb887@cornell.edu
Tobias M Weinberg
Computer Science
Cornell Tech
NYC, New York, USA
tmw88@cornell.edu
Thijs Roumen
Information Science
Cornell Tech
NYC, New York, USA
thijs.roumen@cornell.edu

Abstract

When novices fabricate, they start by choosing a workflow (e.g., laser cutting, 3D printing, etc.) and corresponding software from a narrow set they know. As they advance their design, another workflow might better suit their intent, but their models remain committed to the original workflow. This prohibits exploration, which fosters informed decision-making.

In this paper, we investigate how CAD interfaces can guide exploration and comparison of workflows. Specifically, comparison can advance users' reasoning about design decisions. We developed a prototype interface, CAMeleon, which lets users compare fabrication workflows. Users load 3D models and preview outcomes from different workflows. We hypothesize that presenting alternative outcomes supports exploration and scaffolds informed decision-making. Upon workflow confirmation, CAMeleon allows users export both machine and human instructions for the chosen fabrication workflow.

We interviewed seven fabrication educators to understand how such tools can be integrated into teaching and to demonstrate how we adjust our tool based on their insights. In user evaluation (N = 12), guided comparison helped participants consider a broader range of workflows, reflect on trade-offs, and experiment with new ways of planning.

Citation

Acknowledgments

We thank the fabrication experts who shared their insights and feedback through interviews throughout this research. Their input was invaluable in shaping the direction of this work. We also thank Niti Parikh, Sebastian, and the MakerLab at Cornell Tech for their support.

Contact

If you have questions about this work, contact Shuo Feng at sf522 at cornell dot edu.