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An Inspection Approach for Nonrigid Mechanical Parts

2013· article· en· W2043813414 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.

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFlexibility (engineering)CADCurvatureProcess (computing)EngineeringSheet metalMechanical engineeringStructural engineeringEngineering drawingComputer scienceMathematicsGeometry

Abstract

fetched live from OpenAlex

Nowadays, a complicated and expensive conformation jig is needed to inspect the nonrigid parts. In a free-state condition, these parts may have a significant different shape than their nominal model (CAD) due to gravity loads and residual stress. In this paper, we present a new method for automatic fixtureless inspection of nonrigid parts. The inspection in our case is limited to the profile deviation as required by ASME Y14.5 standard and the defects are dent shapes. Our method combines the curvature estimation, one of the intrinsic properties of the geometry, with the Thomson statistical test in order to identify the defects due to the inherent variations of the manufacturing process from the deformations due to the flexibility of the part. The method is tested and validated on a simulated flexible part representing a typical sheet metal from the transport industry.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.071
GPT teacher head0.355
Teacher spread0.284 · 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