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Record W2424570703 · doi:10.1515/ama-2016-0013

Influence of Tempering on Mechanical Properties of Induction Bents below 540°C

2016· article· en· W2424570703 on OpenAlexaboutno aff
Peter Jankejech, P. Fabian, Jozef Bronček, Yuriy Shalapko

Bibliographic record

VenueActa Mechanica et Automatica · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
Fundersnot available
KeywordsTemperingInduction heatingMaterials scienceInduction hardeningToughnessUltimate tensile strengthBendingComposite materialQuenching (fluorescence)MetallurgyStructural engineeringEngineeringElectromagnetic coilElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The article describes basic principles of induction bending and the change of mechanical properties from as received (straight) pipe made from HSLA steel to induction bend. The main purpose of this article is to experiment with tempering temperatures below 540°C. After tempering at 540°C which is the lowest recommended temperature for post bend heat treatment (PBHT) according to CSA specifications (Canadian Standards Association) the induction bend area in many cases does not achieve the minimum required mechanical properties and therefore it is not accepted for usage. In this article mechanical properties such as tensile, toughness, hardness are evaluated. Also the article contains microstructural analyses and comparison of bended and heat treated samples.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueActa Mechanica et AutomaticaSame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207