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Record W2013184160 · doi:10.1504/ijmpt.2008.022141

Single point incremental forming

2008· article· en· W2013184160 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

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.

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)0.000
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 score0.009
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
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