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Record W2013184160 · doi:10.1504/ijmpt.2008.022141

Single point incremental forming

2008· article· en· W2013184160 on OpenAlexafffund
M. E. J. Ham, Jack Jeswiet

Bibliographic record

VenueInternational Journal of Materials and Product Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaAtsumi International Scholarship Foundation
KeywordsMaterials sciencePoint (geometry)Incremental sheet formingMetallurgyForming processesEngineering drawingComposite materialGeometryEngineeringMathematics

Abstract

fetched live from OpenAlex

Traditional sheet metal forming requires expensive dedicated dies, both positive and negative dies, where each die mimics one side of the desired part. Modern manufacturing industry strives to be flexible and to respond to customer needs. This trend toward flexibility requires new sheet metal forming methods. One method is Single Point Incremental Forming (SPIF) which does not use dedicated dies. SPIF is a sheet metal forming process that was first introduced in the early 1990s. It has gone through a variety of changes since then. This paper will partially review the genesis of SPIF and then discuss experimental results for the parameters: tool size, step size, material type, material thickness and shape. The data will be presented as two dimensional and three dimensional statistical plots, which are created with software called JMP. New information is presented in the form of surface response plots.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.003

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 head0.246
Teacher spread0.230 · 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
GenreEmpirical

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

Citations20
Published2008
Admission routes2
Has abstractyes

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Same venueInternational Journal of Materials and Product TechnologySame topicMetal Forming Simulation TechniquesFrench-language works237,207