A Hybrid Framework for Characterizing and Benchmarking Fatigue S‐N Curves in Aluminum Alloys by Integrating Empirical and Data‐Driven Approaches
Bibliographic record
Abstract
ABSTRACT The complicated and stochastic nature, coupled with uncertainties and data scatter, poses challenges in developing a general fatigue model. This study introduces a hybrid framework that integrates an empirical model with data‐driven approaches, aiming to address data scarcity and streamline the fatigue characterization of aluminum alloys. It was found that support vector regression (SVR) and neural network (NN) exhibit superior performance, with SVR achieving a mean absolute error (MAE) of 0.13 (cycles to failure in log scale) for training and 0.14 for testing, and NN reaching an MAE of 0.15 for training and 0.16 for testing data. The employment of leave‐one‐group‐out‐cross‐validation (LOGOCV) ensured the generalizability of the models, with the effectiveness confirmed by the actual‐predicted life plot. The results demonstrated that almost 98% of predicted data fell within the life factor of ±1. This methodology reduces the requirement for experimentation and provides the prospect of automating fatigue design and characterization.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".