Low Cycle Fatigue Model Selection by Performance Analysis
Bibliographic record
Abstract
A reliable low cycle fatigue (LCF) model requires the selection of appropriate damage mechanism components necessary to accurately approximate experimental life data. In particular, two fundamental tasks have to be performed: firstly, identify the most appropriate model among those available in literature, and secondly, verify that the model is appropriate in relation to the operational conditions of the component whose life is under evaluation (e.g., check if the model accounts for all the relevant damage mechanisms and phenomena). The European Creep Collaborative Committee (ECCC) developed a procedure that supports the researcher in evaluating performances and reliability of creep models, known as Post Assessment Tests (PATs). At the moment, there is no equivalent procedure for low cycle fatigue and ECCC work may provide the LCF researcher with useful guidelines. This paper is intended to investigate, compare and suggest which kind of verification is appropriate to identify any possible misbehavior in a fatigue model or in the LCF tests that supported the model. This procedure involves an analysis of the model performances in terms of comparison with the experimental population, supported by a deep knowledge of the damage mechanisms of the given material. Particular attention will be paid to materials with a particularly high dispersion, such as cast nickel-based superalloys. This paper is also meant to stimulate the fatigue data user community to propose and share methodologies in the perspective of the creation of a recommendation code, similar to what has been done by ECCC for creep.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".