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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 OpenAlexaffvenue
Marc S. Goldstein, J. P. Mitchell, Robert Fleisig, Allan D. Spence

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.003

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

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 designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2011
Admission routes2
Has abstractyes

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