Session: CIE-11-02 CAPPD:Computer-Aided Product and Process (CAPPD General)

Paper Number: 143111

143111 - Quantitative CAD Archetype Framework Evaluation With Professional User Data 

With the prevalence of cloud computing and improvements in computing power, multi-user software platforms, including computer-aided design (MUCAD) softwares, have become more popular. These MUCAD softwares streamline the CAD design process, by enhancing the CAD software capabilities with collaboration features. These MUCAD softwares further enable users to work on CAD parts and projects synchronously and in parallel, live, similar to users editing the same text document at the same time. With this new, powerful technology, understanding the way that users interact with not only the software, but each other, will be critical for increasing productivity and enabling companies and professionals to optimise their workflows. 

Research has shown that there are a number of valid approaches to designing a CAD part, and one might imagine that these differences could be understood and classified such that collaborative compatibility can be improved. In a previous work completed by Zhang et al.~\cite{zhang_developing_2024}, a framework for analysing CAD user ``archetypes'' -- akin to these action tendencies – was created, based on the types of user data that can be collected from modern MUCAD user analytics. Previous studies of CAD user archetypes~\cite{celjak_data-driven_2023,ross-howe_determining_2023} have shown that there exist distinct groups in the CAD workflow, however those studies neglect to use a formal framework and lack quality, professional data in their analyses. 

In this paper, Zhang et al.’s previously developed CAD framework will be applied to a professional engineering dataset. This will be the first test of that framework, which will reveal if the framework is actually feasible for evaluating user archetypes. Additionally, insights may be gained from the framework, which will further provide perspectives on archetypes in the CAD and engineering context. To properly apply the framework, first the dataset is filtered and processed through multiple methods in Python. The cleaned dataset is then used to calculate various metrics, forming the basis of the CAD archetype framework.

From these ratios, data is shown on the distribution of users across each of the CAD archetype dimensions. Further discussion is completed on the data quality, along with what insights the framework offers for the analysis of users. Finally, the paper concludes with discussions on future work and potential extensions on the development of this framework, as well as data analyses.

Presenting Author: KAIWEN ZHANG University of Toronto

Presenting Author Biography: Kevin Zhang is a 4th year undergraduate student at the University of Toronto studying mechanical engineering. He is interested in researching the areas of CAD, product design, and systems engineering. He has held internships previously at Bombardier Aerospace and Zebra Technologies for a total 20 months of experience in project management. He is going to work at Pratt and Whitney under the Patents and Innovation group starting May 2024, and has been accepted the Masters of Applied Science Program at the University of Toronto starting January 2025.

Authors:

KAIWEN ZHANG University of Toronto
Kathy Cheng University of Toronto
Alison Olechowski University of Toronto