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Record W2142688353 · doi:10.24908/pceea.v0i0.3861

THE USE OF RAPID PROTOTYPING IN UNDERGRADUATE DESIGN EDUCATION

2011· article· en· W2142688353 on OpenAlexaffvenueabout
Ralph O. Buchal, D. Phillips

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsRapid prototyping3D printingCADProcess (computing)Virtual prototypingEngineering design processComputer Aided DesignConstruct (python library)Manufacturing engineeringSoftwareSoftware engineeringKey (lock)Computer scienceEngineeringEngineering drawingSystems engineeringSimulationMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

The building and testing of physical prototypes has always been a key phase of the engineering design process. Often, students rush to the prototype stage with insufficient modeling and analysis. As a result, the process resembles “trial and error” more than systematic engineering design. Furthermore, engineering schools lack the facilities and students lack the skills to construct more than very crude prototypes with little resemblance to the CAD models or to the final design. On the other hand, engineering schools typically have state-of-the-art CAD software. As an alternative to physical prototyping, the emphasis is shifting to “virtual” prototyping using CAD models and simulation. Many design attributes like appearance, performance, etc. can be established through simulation with a high degree of reliability. Furthermore, the recent availability of low cost rapid prototyping technology makes it possible to quickly and easily produce physical parts directly from the CAD model. The University of Western Ontario Faculty of Engineering has recently established a rapid prototyping facility for undergraduate design projects. The facility is available to students from all programs and years. The facility is professionally managed by technicians from University Machine Services (UMS). Several rapid prototyping technologies are available, including Fused Deposition Modelling (Stratasys FDM 3000 and Stratasys Vantage SE) and 3D printing (Z-Corp Z510 3D Printer). The Z-Corp 3D printer is capable of processing a batch of parts with a total volume of 1120 cubic inches in 20 hours, at a cost in materials of under $5 per cubic inch. The Z-Corp printer has a resolution of 600 dpi and 256 colours, and is capable of accurately reproducing all the colours on a CAD model including texture maps. To have a part made, students simply save their CAD model as a VRML file, and submit the file for processing. Jobs are batched, and the machine is setup and run by UMS personnel. Some finishing work is completed by the students. The anticipated turnaround time is a day or two, and the typical prototype cost is under $50. These services became available in January 2006. The final paper will include experiences gained over the coming weeks.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.189
Teacher spread0.166 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2011
Admission routes3
Has abstractyes

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