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Record W2001880410 · doi:10.1243/0954405042323559

Analysis of surface roughness for parts formed by computer numerical controlled incremental forming

2004· article· en· W2001880410 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2004
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface roughnessSurface finishMaterials scienceSheet metalShear (geology)Surface (topology)InterferometryNumerical controlForming processesWhite light interferometryComposite materialEngineering drawingMechanical engineeringGeometryOpticsMetallurgyEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Surface roughness tests were performed on computer numerical controlled (CNC) incrementally formed sheet metal parts using various tool depth increments and spindle speeds. A non-contact method using white light interferometry was selected to avoid scratching the material surface. All parts were formed from annealed Al 3003 sheet to a shape with a flat 45° wall section for testing. A relationship was defined between peak-to-valley roughness and depth increment, which was then compared to theory for shear forming. The surface quality of modern incremental methods and shear forming was shown to depend on similar settings, and the defined relationship allows for knowledgeable control of surface roughness in future work on such applications as reflective surfaces.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.222
Teacher spread0.213 · 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