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Experimental characterization and prediction of forming limit diagrams of PHS1800 during hot stamping

2023· article· en· W4380885333 on OpenAlexaff
Pedram Samadian, Ruijian He, Ryan George, C. Butcher, Michael J. Worswick

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHot stampingFormabilityMaterials scienceForming limit diagramStampingDigital image correlationHardening (computing)Composite materialUltimate tensile strengthDeep drawingTensile testingQuenching (fluorescence)BendingBlankAusteniteMetallurgyMicrostructureOptics

Abstract

fetched live from OpenAlex

Abstract The use of press-hardening steels (PHSs) in automotive bodies creates the opportunity of producing thinner, higher-strength components. PHS1800, an Al-Si coated PHS grade with ultimate tensile strength of around 1.8 GPa after hot stamping, is a candidate material for vehicle anti-intrusion applications. The current study aims to investigate the formability of this steel during the hot-stamping process. A custom Marciniak punch test and in situ digital image correlation (DIC) techniques were used to determine the in-plane forming limits of this steel during quenching from an austenitic temperature. The carrier blank thickness and geometry were exploited to quench the surrounding material of the specimens while promoting localization in their central regions. Approximately linear strain paths ranging from uniaxial to biaxial stretching were obtained in the tests while avoiding friction and out-of-plane bending. The forming limit curves (FLCs) of the material under various hot-stamping conditions were then predicted using the Marciniak-Kuczyński (MK) model, taking into account the temperature and strain-rate histories. The predicted limit strains were in good accord with the measured data.

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 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 score0.033
Threshold uncertainty score0.533

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 communication0.0000.001
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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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