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Record W2008552711 · doi:10.14419/ijet.v4i1.3928

Mechanical and metallurgical properties of DP 1000 steel square butt welded joints with GMAW

2014· article· en· W2008552711 on OpenAlexfundno aff
Ianto Rocha, Ivan Guerra Machado, Cíntia Cristiane Petry Mazzaferro

Bibliographic record

VenueInternational Journal of Engineering & Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
FundersInstitute for Catastrophic Loss Reduction
KeywordsMaterials scienceWeldingTemperingMetallurgyUltimate tensile strengthButt jointGas metal arc weldingMartensiteSofteningBase metalComposite materialFlash weldingHeat-affected zoneMicrostructure

Abstract

fetched live from OpenAlex

In this work, Gas Metal Arc Welding (GMAW) was used to study the influence of heat input (i.e. cooling rate) on mechanical/metallurgical properties of square butt welded joints of DP 1000 sheets. The influence of filler metals of different strengths on the mechanical properties of joints was also tested. A significant decrease in hardness was observed in the Heat Affected Zone (HAZ) due to martensite tempering, in regions where peak temperature was close to isotherm AC1 (calculated in 735 oC for these steel); coincidently, fracture in every tensile test occurred in regions where martensite was tempered, even when a wire of less strength was used. It was noticed that the decrease in ultimate tensile and yield strength of base metal was inherent to welding. When minimum heat input was used, deterioration in mechanical properties was less pronounced, once degree of HAZ softening was smaller. Elongation of joints increased with increasing heat input.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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