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Record W1864958779 · doi:10.1115/1.4031900

The Relationship Between Geometrical Complexity and Process Capability

2015· article· en· W1864958779 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

VenueJournal of Manufacturing Science and Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsProcess (computing)NoveltyVariance (accounting)Geometric dimensioning and tolerancingSimple (philosophy)Function (biology)MathematicsComputer scienceAlgorithmEngineering drawingGeometryEngineering

Abstract

fetched live from OpenAlex

This paper proposes a new method to estimate the process capability for a profile geometric tolerance as defined by the ASME Y14.5 standard. The novelty of the method is that it uses the known process capability of a given geometry to predict, using the order statistics theorem, new capabilities for different geometries of higher or lower complexity. By considering the geometrical complexity of mechanical parts, a manufacturing process may be capable (e.g., Cpk > 1.5) for parts with simple geometry and incapable (e.g., Cpk < 1) for parts with complex geometry. In the proposed model, the process capability becomes a mathematical function of both the statistical behavior of the process (e.g., expectation and variance) and the geometric complexity of manufactured surfaces. Three experimental case studies are presented to illustrate the usefulness and the validity of the developed model.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.265
Teacher spread0.212 · 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