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Record W4389584919 · doi:10.17118/11143/21062

Investigating hydrogen embrittlement in aerospace martensiticsteels

2023· article· en· W4389584919 on OpenAlexaff
Johnny Adukwu, Rajwinder Singh, Roger Eybel, Mamoun Medraj

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsSafran Electronics (Canada)Concordia University
Fundersnot available
KeywordsMaterials scienceHydrogen embrittlementAerospaceMetallurgyEmbrittlementMartensiteMicrostructureCorrosionEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

The structural integrity of aerospace components depends on material composition and the ability to withstand harsh and aggressive environments without much degradation under heavy loading conditions. Landing gears are essential components used in the aerospace industry which are typically made from high-strength martensitic steels. SAE 4340 steel is commonly used in the manufacture of landing gears due to the combined properties of high strength and toughness. However, this grade of steel can become brittle during service in an environment which can expose it to hydrogen embrittlement (HE). On the other hand, steel grades like 300M are promising alternatives to withstand heavy loads and resist HE. Therefore, there has been a recent drive in the aerospace industry to select which steel grade is more suitable to withstand HE in harsh environment. In this work, the response and resistance to HE of these two high-strength martensitic steels for aerospace applications were studied using electrochemical hydrogen permeation and small-scale shear punch mechanical testing techniques. First, circular disc samples (13 mm diameter, 0.6 mm thickness) of both 4340 and 300M were prepared from annealed steel bars in batches of 10 and subjected to heat treatments to obtain tempered martensitic steel microstructures conforming to the industry standards used for the manufacturing of landing gears. Next, the heat-treated and surface-polished disk samples were electrolytically charged with hydrogen using a DevanathanStachurski double-cell set up at different current densities to simultaneously examine the hydrogen diffusivity. Shear punch experiments were then performed to investigate the difference in ductility reduction and strength loss between the hydrogencharged and uncharged samples for the two steel types (4340 and 300M). Further investigations into the failure modes of the punched discs due to HE were conducted using SEM and EBSD characterization techniques. The results of the electrochemical hydrogen permeation curves show that 300M had lower hydrogen diffusivity than 4340. This indicates that 300M prevents easy diffusion of hydrogen through its microstructure compared to 4340 and likely reduces the amount of diffusible hydrogen into critical areas containing defects. Consequently, the shear punch test results show that 300M outperforms 4340 in terms of strength loss and reduction in ductility. With regard to the type of failure, mostly shear-ductile dimples were observed in uncharged specimens. On the other hand, brittle-like fracture surfaces and some secondary cracking were observed in hydrogen-charged punched steels, while 4340 experienced more severe damage when charged with hydrogen at a higher current density compared to the mechanical response of 300M.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.033
GPT teacher head0.286
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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