Analysis of Collaborative Assembly in Multi-User Computer-Aided Design
Bibliographic record
Abstract
Abstract Cloud-based multi-user computer-aided design (MUCAD) tools have the potential to revolutionize design team collaboration. Previous research focusing on parametric part modeling suggests that teams collaborating through MUCAD are more efficient at producing a CAD model than individual designers. While these studies are enlightening, there is a significant gap in understanding the impact of MUCAD on assembly modeling, despite assembly’s crucial role in the design process. Part and assembly models are both defined by parametric relationships, but assembly models lack hierarchical feature dependency; we propose that by modularizing tasks and executing them in parallel, teams can optimize the assembly process in ways not possible with part modeling. Our study aims to examine and compare CAD assembly performance between individuals and virtual collaborative teams using the same cloud MUCAD platform. Through analyzing team communication, workflow, task allocation, and collaboration challenges of teams comprising 1–4 members, we identify factors that contribute to or hinder the success of multi-user CAD teams. Our results show that teams can complete an assembly in less calendar time than a single user, but single users are more efficient on a per-person basis, due to communication and coordination overheads. Notably, pairs exhibit an assembly bonus effect. These findings provide initial insights into the realm of collaborative CAD assembly work, highlighting the potential of MUCAD to enhance the capabilities of modern product design teams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".