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Record W7096718631

Introducing a CAD/CAM Laboratory to Support an Undergraduate Course in Manufacturing Engineering

2011· article· en· W7096718631 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumProduct (mathematics)Course (navigation)MachiningSoftwareEngineering educationTechnical universityProduction engineering
DOInot available

Abstract

fetched live from OpenAlex

Contemporary courses in manufacturing engineering can easily become susceptible to obsoleteness due to the dramatically reduced lifespan of most current technical information caused by the rapid advances in information technology in recent years. One of the best ways to prevent this problem and preserve the practical value of the courses in manufacturing engineering is to permanently include state-of-the-art technical contents into the course curricula and respective laboratories. In this context, the Department of Mechanical and Industrial Engineering (MIE) at the University of Toronto developed a new three-module laboratory to support the curriculum of its undergraduate course in manufacturing engineering. The initial laboratory module introduces a CAD/CAM software package, which is subsequently used by the students to first design a plastic product and the corresponding injection mold, and then generate the G-code needed for the machining of the mold. The second module exposes the students to the use of a CNC milling machine in order to produce the mold, while the final module provides them the opportunity to produce the plastic product using the mold and an injection molding machine. This paper gives emphasis to the technical content, the projected educational objectives and hands-on experience, and the appropriate educational approaches required for the successful implementation of this laboratory. 1.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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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