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Record W2116158268 · doi:10.5539/jel.v3n3p172

Enhancing Manufacturing Process Education via Computer Simulation and Visualization

2014· article· en· W2116158268 on OpenAlexvenueno aff
Priyadarshan Manohar, Sushil Acharya, Peter Wu

Bibliographic record

VenueJournal of Education and Learning · 2014
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
Fundersnot available
KeywordsVisualizationProcess (computing)Computer scienceScheduleManufacturing engineeringIndustrial engineeringEngineering drawingEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Industrially significant metal manufacturing processes such as melting, casting, rolling, forging, machining, andforming are multi-stage, complex processes that are labor, time, and capital intensive. Academic researchdevelops mathematical modeling of these processes that provide a theoretical framework for understanding theprocess variables and their effects on productivity and quality. However it is usually difficult to provide thestudents with hands-on experience of experimentation with process parameters which leads to disconnectbetween engineering education and industrial needs. In order to solve this problem, interdisciplinary studentprojects were undertaken at author’s institution to develop computer simulation tools that would facilitateprocess visualization, experimentation, exploration, design and optimization. The hypothesis is that these newcomputer-based tools would enhance educational experience for the manufacturing engineering students asassessed by the ABET-derived educational outcomes and also based on Bloom’s cognitive outcomes modifiedfor STEM disciplines.The first system described in this paper is the visualization of metal ingot production schedule in an industrialsetting that provides a basis for interactive decisions. The graphical user interface is created to visualize theschedule according to the specific characteristics of the machines. Another example of process simulationpresented in this paper is the design and analysis of flexible rolling technology in industrial processing of lowcarbon steels. Process simulation tools designed in both cases allow new process sequences to be generated bybreaking down existing process routes into key elements and then by recombining them to generate novelalternative and more efficient hot processing sequences. This enables the identification of an optimal processsequence for specified steel compositions that also satisfies simultaneous design criteria such as processfeasibility and property maximization. It is proposed that incorporation of such computer simulation tools in thepedagogy would be highly effective to enhancing and enriching undergraduate manufacturing education.

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 score0.708
Threshold uncertainty score0.247

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 head0.006
GPT teacher head0.272
Teacher spread0.267 · 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
Published2014
Admission routes1
Has abstractyes

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