Evaluating the Effect of Hydrogen on the Tensile Properties of Cold-Finished Mild Steel
Bibliographic record
Abstract
One of the major sources of catastrophic failures and deterioration of the mechanical properties of metals, such as ductility, toughness, and strength, in various engineering components during application is hydrogen embrittlement (HE). It occurs as a result of the adsorption, diffusion, and interaction of hydrogen with various metal defects like dislocations, voids, grain boundaries, and oxide/matrix interfaces due to its small atomic size. Over the years, extensive effort has been dedicated to understanding hydrogen embrittlement sources, effects, and mechanisms. This study aimed at assessing the tensile properties, toughness, ductility, and susceptibility to hydrogen embrittlement of cold-finished mild steel. Steel coupons were subjected to electrochemical hydrogen charging in a carefully chosen alkaline solution over a particular time and at various charging current densities. Tensile property tests were conducted immediately after the charging process, and the results were compared with those of uncharged steel. The findings revealed a clear drop in toughness and ductility with increasing hydrogen content. Fracture surfaces were examined to determine the failure mechanisms. This evaluation has enabled the prediction of steel’s ability to withstand environments with elevated hydrogen concentrations during practical applications.
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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) | 0.000 | 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".