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Record W4404617758 · doi:10.3390/engproc2024076089

A Review of Digital Projection and Measurement Technologies for Industrial Fabrication Quality Assurance

2024· review· en· W4404617758 on OpenAlexaff
Graeme Drysdale, Graham Corsar

Bibliographic record

Venuenot available
Typereview
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsSaskatchewan Polytechnic
Fundersnot available
KeywordsQuality assuranceComputer scienceFabricationProjection (relational algebra)Quality (philosophy)EngineeringOperations managementPhysics

Abstract

fetched live from OpenAlex

Industrial fabricators are increasingly digitising the acquisition of measurement data to expedite the quality assurance process and reduce labour costs. The premise of this study is to clarify the fundamentals behind the workflow of projecting images to perform accurate dimensioning during the fabrication process and collection of measurement data during the quality assurance process. This study compares digital devices used to superimpose or measure dimensions during the fabrication and quality assurance steps in the manufacturing process. The physical characteristics of the positioning and measurement technologies are described.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.299
GPT teacher head0.416
Teacher spread0.118 · 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 designNot applicable
Domainnot available
GenreReview

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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