A novel stress coupled hydrogen failure criterion to predict hydrogen embrittlement in high-strength steels
Bibliographic record
Abstract
Hydrogen embrittlement (HE) has been a long-standing challenge for high-strength metals, particularly in high-strength steels. The present work focuses on developing a modeling approach that accurately captures and predicts the HE induced failure initiation in high-strength steels. A stress-coupled diffusion model is formulated and implemented within the finite element modeling framework to capture hydrogen distribution under different loading and charging conditions. Benchmarking the model against the experimental results obtained from incremental step load testing on AISI 4340 notched specimens under varying charging scenarios, the local hydrogen concentration and stress state has been identified as important parameters manifesting material degradation due to HE. This refurbishes the conventional concept of critical hydrogen concentration alone required to induce HE by establishing a strong dependency of hydrogen concentration on the history of loading and stress state. A novel HE failure criterion is developed considering the coupling aspects of local hydrogen concentration and stress triaxiality into a cohesive zone model. The model is then validated by considering different test sets exhibiting the applicability of the proposed model to different loading conditions. The predictability of our model is also further tested by its application to other high-strength steels besides AISI 4340, further demonstrating its generic utility and robustness to capture HE failure under different testing conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".