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Record W4309865900 · doi:10.1088/2053-1591/aca580

Fractography analysis into low-C steel undergone through various destructive mechanical tests

2022· article· en· W4309865900 on OpenAlex
Saurabh Dewangan, Senthil Kumaran Selvaraj, B. Karthikeyan, Utkarsh Chadha, Prakrit Singhal, Partha Pratim Sarma, Parthasarathyraam Raju, Uday Kumar, M. Pradeep Kumar

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

VenueMaterials Research Express · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFractographyMaterials scienceAusteniteMetallurgyComposite materialAnnealing (glass)Tensile testingFracture toughnessUltimate tensile strengthCarbon steelMicrostructureCorrosion

Abstract

fetched live from OpenAlex

Abstract The present work deals with a critical fractographic analysis into low carbon (0.18%-C) steel samples which were used for three different mechanical tests: tensile test; shear test; and toughness test. These mechanical tests were performed in standard sized specimens as recommended by ASTM. In each category of test, there were two different specimens with different physical states according to heat treated conditions. First specimen was in ‘as received’ condition and another was annealed. For annealing, sample was first heated up to austenitic temperature and inserted inside the sand for slow rate of cooling. As these two categories of samples were undergone through destructive tests, the variation in fracture behaviour of the samples was analysed by FESEM, XRD. A significant variation in fractographic images could be observed in different heat-treated samples. Micro-pores, dimples, cleavage facet, peaks, valleys, and cave formation were observed in the 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.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
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 score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0320.000

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 head0.051
GPT teacher head0.364
Teacher spread0.313 · 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