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Record W1929848733 · doi:10.24908/pceea.v0i0.3726

REVERSE ENGINEERING: TENSION RATCHET DISSECTION PROJECT

2011· article· en· W1929848733 on OpenAlexvenueno aff
Jeremiah Vanderlaan, Josh Richert, James R. Morrison, Thomas E. Doyle

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsReverse engineeringPaceGraphicsProcess (computing)Computer scienceBenchmark (surveying)Engineering design processSoftware engineeringProduct (mathematics)SoftwareCollaborative engineeringProduct designCornerstoneEngineering drawingEngineeringWork in processMechanical engineeringOperations managementComputer graphics (images)

Abstract

fetched live from OpenAlex

We are a group of engineering students, in our first year of undergraduate study. We have been selected from one thousand first year students and have competed and won the PACE competition. All engineers share a common general first year, but we have been accepted into Civil and Mechanical engineering. This project was assigned as the final project in the Design and Graphics course. The project we are tasked with, called the Cornerstone Design Project, is to first dissect a product, discover how it works, dimension each part and create a fully assembled model using CAD software (Solid Edge V20 in our case). As part of discovering how it works we must benchmark it so the device can be compared with competing products. The goal of the project is to develop a full understanding of part modeling and assembly in Solid Edge, learn proper measurement techniques, and learn the process of reverse engineering and product dissection. All of these tasks were stepping stones to help us fully understand how the device, and all its components, 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 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)0.000
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 score0.228
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.178
Teacher spread0.169 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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