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Record W2965468646 · doi:10.1017/dsi.2019.144

An Exploratory Study Comparing CAD Tools and Working Styles for Implementing Design Changes

2019· article· en· W2965468646 on OpenAlexaff
Vrushank Phadnis, Kevin Leonardo, David R. Wallace, Alison Olechowski

Bibliographic record

VenueProceedings of the ... International Conference on Engineering Design · 2019
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCADComputer scienceDependency (UML)ProductivityTree (set theory)Value (mathematics)Collaborative designExploratory researchHuman–computer interactionEngineering drawingSoftware engineeringMachine learningEngineeringSystems designMathematics

Abstract

fetched live from OpenAlex

Abstract This paper presents the findings of a preliminary study comparing implementation of design changes using various computer-aided design (CAD) working styles. Our study compares individuals’ and pairs’ completion of a series of changes to a toy car CAD model. We discuss the results in terms of productivity and value added ratio, derived from time-based quantitative data. We also discuss qualitative findings acquired through post-study surveys. Overall, our findings suggest that pairs were less efficient than individual designers due to overheads like communication, history dependency and complex couplings within the CAD model tree. However, it is also noteworthy that within each pair the lead participant's performance was at par with individual participants. Lastly, we also discuss behaviors and patterns that emerge as unique to the synchronous collaborative environment, motivating future work.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.150
GPT teacher head0.304
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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