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Record W2088331718 · doi:10.1002/srin.201200288

Driving Force and Logic of Development of Advanced High Strength Steels for Automotive Applications

2013· article· en· W2088331718 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

Venuesteel research international · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTwipAutomotive industryFormabilityMaterials scienceFerrite (magnet)MetallurgyHigh strength steelManufacturing engineeringAusteniteEngineeringComposite materialMicrostructure

Abstract

fetched live from OpenAlex

Abstract The major scientific and technological advances and breakthroughs of advanced high strength steels (AHSS) were achieved due to the strong demands of automotive industry. The development of AHSS began in the early 1980s with the aim of improving passenger safety and weight‐saving. The present paper presents the driving forces and logic of development of various AHSS for automotive applications since 1980s. The importance of crash performance, weight‐saving, formability, and rigidity are critically reviewed for the development of new steel grades for automotive applications. The logical sequences of the development of dual phase (DP) steel, transformation induced plasticity (TRIP) steels, tempered DP steels, complex phases (CP) steels, Ferrite‐Bainite steels, hot‐stamping technology, twinning induced plasticity (TWIP) steels, Quench and Partitioning (Q&P) steels, Medium Mn steels, and steels–polymer composites are presented.

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 score0.181
Threshold uncertainty score0.272

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 head0.028
GPT teacher head0.308
Teacher spread0.280 · 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