Hydrogen Embrittlement Susceptibility in High Strength Aerospace Structural Steels
Bibliographic record
Abstract
Martensitic structural steels are widely used in aerospace components due to the strength-to-weight ratio requirement. However, hydrogen embrittlement (HE) poses a significant threat to the structural integrity of these components. Aerospace components, such as landing gears, can become embrittled by hydrogen ingress during production processes such as electroplating or when operating in corrosive environments. Despite mitigation measures such as post-plating baking, internal hydrogen still persists in the microstructure of the steel substrates, which could lead to catastrophic failures when subjected to high in-service loads. Thus, investigating how hydrogen interacts with the microstructure of high strength structural steels is critical for developing HE resistant steel materials for aerospace applications. Quenched and tempered 4340 steel is commonly used in landing gears due to its strength and toughness. 300M, a modified version of 4340, is designed to withstand comparatively high stresses. However, it is imperative to investigate the performance of these steels when subjected to HE conditions. In this study, electrochemical permeation and shear punch techniques are employed to assess their performance at various hydrogen concentrations. Apparent hydrogen diffusivity was significantly higher in 4340 compared to 300M as measured using Devanathan-Stachurski permeation setup under different charging conditions. Consequently, the severity of HE failure was more in 4340 compared to 300M as evaluated by the shear punch testing. The variation in hydrogen-induced failure can be attributed to the differences in formation of second-phase precipitates due to the varying alloy compositions between the subject steels. Insights from this study will advance the improvement of martensitic steels for aerospace structural application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".