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Introduction of a 1000 MPa crush tip within a Usibor® 1500-AS axial crush rail using in-die heated hot stamping

2018· article· en· W2893830924 on OpenAlexafffund
Matthew Tummers, Kaab Omer, A. Abedini, Cale Peister, C. Butcher, Michael J. Worswick, Skye Malcolm, Cyrus Yau, Ron Soldaat

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsArcelorMittal (Canada)University of Waterloo
FundersCanada Research Chairs
KeywordsStructural engineeringFinite element methodLS-DYNAMaterials scienceCrashworthinessUltimate tensile strengthPlane stressBendingComposite materialEngineering

Abstract

fetched live from OpenAlex

Axial crush is an important mechanism used in automotive front end structures to absorb impact energy. In this work, numerical simulation is used to investigate the crush response of a Usibor® 1500-AS axial crush rail with tailored properties achieved using in-die heated (IDH) hot stamping. In this case, the targeted properties in the softened zone correspond to a tensile strength of 1000 MPa. A finite element model is utilized to predict the crash performance of the tailored in-die heated 1000 MPa crush tip within a Usibor® 1500-AS rail. A numerical parametric study is presented, comparing the crush response based on fracture loci determined using two different plane-strain strain experiments, one the plane-strain Nakazima dome test and the other the VDA-238 V-bending test. In addition a number of different mesh regularization treatments are considered. The predictions exhibit a strong dependency of the onset of fracture upon the plane strain fracture strain level and degree of mesh regularization.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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