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Effect of Heat Input on Weldability of Low Nickel High Manganese Stainless Steel.

2023· article· en· W4383094584 on OpenAlexaff
Ahmed Salem Eid, mohamed Abd allatif, Salwa H. El‐Sabbagh, Hossam Halfa

Bibliographic record

VenueInternational Journal of Materials Technology and Innovation · 2023
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsCanadian MPS Society for Mucopolysaccharide and Related Diseases
Fundersnot available
KeywordsWeldabilityManganeseMetallurgyMaterials scienceNickelWelding

Abstract

fetched live from OpenAlex

The effect of heat input on the microstructure and mechanical properties of low nickel-high manganese stainless steel welded using a shielded metal arc was investigated. Six samples divided equally into two groups that represented the two distinct degrees of heat input were conducted: low heat (128 J/mm- 139 J/mm-165 J/mm) and high heat (182 J/mm -207 J/mm- 225 J/mm), respectively. These groups were subjected to microstructural analyses and tensile tests to see how heat input affected their joints' mechanical characteristics and microstructure evolution. In the second group, the high value of the ultimate tensile strength (UTS) was found in the case of the high-heat input (182 J/mm) sample. Macrostructure observations were made at the melting zone for each sample. In the case of low heat input formation, partial penetration was observed. However, in the case of high heat input formation, full penetration was observed in all samples treated to the various heat inputs. No defects, such as cracks or voids, were found. In addition, it was observed by increasing heat inputs that the average inter-dendritic spacing in the weld zone increased, which plays a significant role in the observed changes in the tensile characteristics of the welded 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 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)0.001
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 score0.019
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.265
Teacher spread0.258 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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