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Record W3153135787 · doi:10.5267/j.esm.2021.2.001

The impact of heat input on the mechanical properties and microstructure of High Strength Low Alloy steel welded joint by GMA welding process

2021· article· en· W3153135787 on OpenAlexvenueno aff
Saadat Ali Rizvi, Rajnish Singh, Saurabh Kumar Gupta

Bibliographic record

VenueEngineering Solid Mechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceWeldingMicrostructureUltimate tensile strengthIndentation hardnessGas metal arc weldingMetallurgyHeat-affected zoneAlloyComposite materialJoint (building)High-strength low-alloy steelStructural engineering

Abstract

fetched live from OpenAlex

The basic aim of this study was to find a relationship between heat input and mechanical properties of high strength low alloy steel (HSLA) welded joints and also elaborate its effect on microstructure. The combined effect of welding current, voltage and speed i.e. Heat Input on mechanical properties of High Strength Low Alloy Steel (ASTM A242 type-II) weldments have been studied in the present work. HSLA steel work pieces were welded by Gas metal arc welding (GMAW) process under varying welding current, arc voltage, and welding speed. Total nine samples were prepared at different heat input level i.e. 1.872 kJ/mm, 1.9333 kJ/mm, 2.0114 kJ/mm, 2.1 kJ/mm, 2.1956 kJ/mm, 2.296 kJ/mm, 2.4 kJ/mm, 2.5067 kJ/mm and 2.6154 kJ/mm It was observed that as heat input increases the ultimate tensile strength and microhardness of weldment decreased while impact strength increased and it was also observed that on increasing the heat input grain size of microstructure tends to coarsening it is only due to decreasing in cooling rate.

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 score0.247
Threshold uncertainty score0.646

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 head0.009
GPT teacher head0.213
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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEngineering Solid MechanicsSame topicWelding Techniques and Residual StressesFrench-language works237,207