Some (Team) Assembly Required: An Analysis of Collaborative Computer-Aided Design Assembly
Bibliographic record
Abstract
Abstract Previous efforts in the area of collaborative computer-aided design (CAD) suggest that a team of designers working synchronously in a multi-user CAD (MUCAD) environment can produce CAD models faster than a single user. Our research is the among the first to investigate assemblies in MUCAD. Due to the lack of hierarchical feature dependency in assemblies, we propose that CAD teams can optimize assembly through modularization and parallel execution. In our study, 20 participants were tasked with assembling pre-modelled CAD parts of varying complexity in teams of one, two, three or four. We analyze audio recordings, team activity, and survey responses to compare the performance of individuals and virtual collaborative teams during assembly, while working with the same MUCAD platform. This paper features a multimodal approach to analyze team trends in communication, workflow, task allocation and challenges to determine which factors are conducive to the success of a multi-user CAD team and which are detrimental. In our work, the success of a team is measured by its productivity score, which is the number of mates added by a team within a given time frame. We present evidence that teams can complete an assembly in less calendar time than a single user, but single users are more efficient based on person-hours, due to communications and coordination overheads. Surprisingly, paired contributors exhibit an assembly bonus effect. These findings represent a preliminary understanding of collaborative CAD assembly work. Our work supports the claim that collaborative assembly activities have the potential to improve the capabilities of modern product design teams, delivering products faster and at lower cost. We identify areas for future research, and highlight areas of improvement for collaborative CAD platforms and engineering design teams.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".