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Record W2758756449 · doi:10.2320/matertrans.m2017093

Influence of Electron Beam Irradiation on Surface Roughness of Commercially AISI 5140 Steel

2017· article· en· W2758756449 on OpenAlex

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 TRANSACTIONS · 2017
Typearticle
Languageen
FieldEngineering
TopicPulsed Power Technology Applications
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceSurface roughnessSurface finishIrradiationMetallurgyCathode rayImpact craterSurface (topology)Composite materialElectronGeometry

Abstract

fetched live from OpenAlex

In this study, a commercially AISI 5140 steel with two different levels of initial surface state (initial high-roughness (IHR) and initial low-roughness (ILR)) was surface-treated by large-area pulsed electron beam irradiation (LPEBI). Surface morphology in 2D and 3D of the two types of samples after LPEBI treatment was characterized. The results show that the surface roughness of IHR samples decreases clearly with increasing LPEBI pulse numbers, while an opposite trend is found in the ILR samples. It is considered that the final surface roughness is influenced by surface remelting and formation of crater-like structures (CLSs) in local regions. For the IHR samples with amounts of mechanical scratches, the remelting plays the leading role, owing to the high surface energy which provides extra driving force for remelting during LPEBI. In contrast, such extra driving force is less due to the relatively flat surface of the ILR samples, instead remelting the surface suffers from the formation of CLSs in localized region. Compared to the formation of localized CLSs, the surface remelting is beneficial to surface roughness of the LPEBI processed 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)0.000
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 score0.201
Threshold uncertainty score0.556

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 head0.008
GPT teacher head0.240
Teacher spread0.233 · 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