Fatigue strength prediction of 410NiMo stainless steel with surrogate weld discontinuities
Bibliographic record
Abstract
• A novel FCAW robotized method introduced controlled slag-like flaws in 410NiMo. • First study combining FCAW, CT scan, and NSIF for fatigue strength prediction. • Proposed framework to evaluate the severity of surrogate slag-like discontinuities. • High-resolution CT scan enables 3D flaw characterization for FE modelling. • Stress field simulations improve NSIF framework for fatigue strength predictions. The fatigue strength of martensitic stainless steel 410NiMo, featuring slag-type discontinuities, was investigated to evaluate the accuracy of fatigue strength prediction models. This study examines the influence of volumetric discontinuities introduced through robotic FCAW on fatigue strength, characterized using advanced techniques. High-resolution CT scanning (20 µm/voxel) enabled precise 3D modelling of the welded zones, supporting finite element simulations of the stress field. These simulations revealed complex distributions, with singularity exponents ranging from 0.27 to 0.45, lower than the typical 0.50 exponent associated with cracks. Fatigue experiments demonstrated that Linear Elastic Notch Mechanics (LENM), applied for the first time in this context, overestimated fatigue resistance by 34 %, whereas Linear Elastic Fracture Mechanics (LEFM), based on maximum discontinuity width, provided more accurate and conservative predictions, with an average deviation of 16 %. Fractographic analyses identified crack initiation sites at flux-filled micro-notches undetectable by CT resolution, while foreign elements from flux residues were observed, suggesting a potential embrittlement effect. These findings indicate that large, rounded discontinuities in welds can behave like cracks due to embedded microscopic cracks or notches. The study highlights the limitations of LENM for volumetric discontinuities in FCAW welds and establishes a framework for improving fatigue strength prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".