MétaCan
Menu
Back to cohort
Record W4385847717 · doi:10.1016/j.aej.2023.07.045

Advances on Incremental forming of composite materials

2023· article· en· W4385847717 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

VenueAlexandria Engineering Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsForming processesSingle pointComposite numberProcess (computing)Incremental sheet formingDeformation (meteorology)Process engineeringMechanical engineeringComputer scienceMaterials scienceManufacturing engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Single Point Incremental Forming (SPIF) is an emerging materials processing technology. Owing to a number of merits, like reduced tooling, cycle time and cost and ability to produce sculptured profiles, in comparison to the traditional methods, the recent past has witnessed a growing interest in the application of SPIF to composite materials. This article authoritatively reviews the advancements in this area made since 2008. The review covers several aspects of the process including deformation characteristics, forming limits, failure modes forming forces, strain recovery, surface quality and analytical models. The current state of the technology and challenges are revealed, and many valuable findings are identified thereby setting guidelines for the process users.

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 score0.153
Threshold uncertainty score0.568

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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