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Record W2149755688 · doi:10.24908/pceea.v0i0.3965

DESIGN OF A CLOSE-UP STEREO VISION BASED SHEET METAL INSPECTION SYSTEM

2011· article· en· W2149755688 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGridSheet metalComputer visionVisual inspectionArtificial intelligenceSquare tilingSample (material)Etching (microfabrication)Computer scienceComputer graphics (images)EngineeringMaterials scienceGeometryStructural engineeringMathematicsComposite materialPhysics

Abstract

fetched live from OpenAlex

This paper describes the design and implementation of a close-up stereo computer vision based sheet metal strain and geometry inspection system. A square grid pattern is first applied to the part using either electrochemical etching or screen printing. The part is formed, and measured using a two camera sensor that can be attached to a CMM or FARO arm. Image processing is used to extract the corners of the deformed grid for either three dimensional reconstruction, or for strain analysis. Graphical results are conveniently displayed using HOOPS. Sample measurements using both steel and aluminum test domes indicate an achievable accuracy comparable to other systems.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.016
GPT teacher head0.195
Teacher spread0.179 · 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