Microstructure-Based Computational Fatigue Life Prediction of Structural Materials
Bibliographic record
Abstract
Conventionally, engineers have to perform fatigue testing, either in stress or in strain-controlled mode, to determine the fatigue properties of a material, which costs a great deal of time and money.Therefore, the concept of computational fatigue design has been proposed and received an increasing interest in recent years.In this research, a microstructure-based computational fatigue design model, named TMW model, is further studied first by using it to predict the fatigue crack nucleation lives of eight different alloys and steels, and comparing the predicted lives with the calculated values from the Coffin-Manson-Basquin relations which are obtained from experimental data fitting.Second, this model is improved by developing the mathematical expressions of the surface roughness factor in the TMW model in terms of the arithmetical mean deviation of the assessed profile which can be determined experimentally, thus making the TMW model more applicable.In addition, a microstructure-based finite element analysis (FEA) model is created to investigate the effect of microstructural inhomogeneity (grain orientation) on the fatigue crack nucleation life of nickel-based alloy Haynes 282 in different strain ranges from low cycle fatigue (LCF) to high cycle fatigue (HCF) at different stress amplitudes.Grain orientations are randomly assigned to a material representative volume element (RVE) with 20 random functions created for both HCF and LCF simulations.The TMW model shows effectiveness for predicting the fatigue crack nucleation life of structural materials.The FEA simulation reveals that potential fatigue crack nucleation sites are likely to occur at
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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 | 0.001 | 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".