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Record W4400089165 · doi:10.23977/jemm.2024.090119

Construction and Implementation of an Integrated Teaching Model for CNC Lathe, CNC Milling, and Multi-Axis Machining

2024· article· en· W4400089165 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Educational Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMachiningCnc millingNumerical controlManufacturing engineeringEngineering drawingEngineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

With the advent of the Industry 4.0 era, CNC technology education faces new challenges and opportunities. This research conducts an in-depth analysis of the educational needs in CNC lathe, CNC milling, and multi-axis machining technologies, discussing the crucial role of an integrated teaching model in enhancing teaching efficiency and meeting industrial demands. By systematically constructing and implementing this model, this paper aims to address current challenges in educational processes, such as maintenance of technical equipment, insufficient teaching resources, and lack of faculty training. The study shows that by integrating teaching resources, improving laboratory facilities, implementing faculty training programs, and designing student incentive mechanisms, it is possible to effectively enhance teaching quality and meet industry demands.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.359
Teacher spread0.334 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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