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Record W2133829972 · doi:10.24908/pceea.v0i0.3594

Integration of Computer Aided Design and Manufacturing at Second Year level in Undergraduate Program

2011· article· en· W2133829972 on OpenAlexaffvenueabout
Christopher Laing, Subramaniam Balakrishnan

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer Aided DesignCADComputer scienceComputer graphicsComputer-aided manufacturingProcess (computing)Rapid prototypingGraphicsKey (lock)Engineering design processEngineering drawingSoftware engineeringEngineering managementManufacturing engineeringEngineeringMechanical engineeringArtificial intelligenceComputer graphics (images)

Abstract

fetched live from OpenAlex

Computer Graphics for Mechanical and Manufacturing Engineering at the University of Manitoba has evolved from a generic first year program to a second year design course that simulates real world experience through the development of a team based project. This challenging and comprehensive program integrates two separate course into one, CAD (Computer Aided Design) and Computer Aided Manufacturing (CAM) Concepts. The latter provides a framework to the process of computer-controlled manufacturing with an emphasis on use of advanced computerized machines. The CAD portion is taught in three stages; the first stage builds proficiency in 3D modeling techniques to create working virtual models. The second stage focuses on the language and communication of mechanical design, and the third stage develops practical skills on a host of modern technology, including CNC (Computer Numerical Control) and rapid prototyping equipment. The second part provides a series of lectures establishing the link between CAD and CAM and culminates in a design project that links the two modules. These complementary programs work hand in hand to provide the necessary theory and practical skill for the students to work in teams, to develop and conclude the term with a working device that they designed, fabricated and tested in one of the two production laboratories allocated to this course. At the conclusion of this program, each device is authenticated to ensure that it has met the criterion which validates both the design as well as the student’s learning experience. This paper will endeavour to be candid in sharing our experiences with you. We will cover everything from looking at the history behind this program, to the challenges we met. Most notably, the number of students compared to the equipment available in both the learning and production stages of this course. We will also explore the principals and thinking that constituted the curriculum with the goal that each facet of learning, both in its theory and application directly relates to real world practice that would contribute to the student’s field of study and their preparation for industry.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0340.015

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 head0.017
GPT teacher head0.194
Teacher spread0.177 · 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 designNot applicable
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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