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Boriding and boronitrocarburising effects on hardness,wear and corrosion behavior of AISI 4130 steel

2019· article· en· W2946109751 on OpenAlexaff
Ramdane Nora, Touhami Mohamed Zine, Abdelkader Khettache, Youcef Khelfaoui, Ali Ourdjini, Xin Jiang

Bibliographic record

VenueMatéria (Rio de Janeiro) · 2019
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Ottawa
FundersUniversität Siegen
KeywordsBoridingMaterials scienceMicrostructureMetallurgyCorrosionScanning electron microscopeHardnessBorideIndentation hardnessVickers hardness testComposite material

Abstract

fetched live from OpenAlex

The effects of boriding and boronitrocarburising surface treatments on the microstructure and mechanical properties of AISI 4130 steel have been investigated. Pack boriding was carried out at 1223 K for 5 h and 8 h, while the double treatment of boronitrocarburising consisted of salt bath nitrocarburising at 853 K for 6 h followed by pack boriding at the same temperature and times as the boriding treatment. Microstructure examination of the borided and boronitrocarburised layers was carried out using optical and scanning electron microscopy, and phase identification was obtained using X-ray diffraction. Vickers hardness, wear and corrosion weight loss tests were used to evaluate the mechanical properties and corrosion behaviour. The results of X-ray diffraction analysis revealed the presence of superhard phases such as FeB, Fe2B, CrB, MnB and MnB2, indicating that pack boriding surface treatment conducted at 1223 K for 8 h exhibited the highest hardness, low coefficient of friction and the greatest wear resistance. Boronitrocarburising treatment, on the other hand, caused a reduction in both the depth of iron borides zone and mechanical properties compared to the boriding surface treatment. These recommended treatments offer to industrialists a particular contribution and interest in the development of these steels, which can be applied in various fields.

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 score0.041
Threshold uncertainty score0.749

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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