High-temperature tribological performance of stir-cast and heat-treated EV31A magnesium alloy: Experiments and predictions
Bibliographic record
Abstract
The temperature effect on the wear behaviour of EV31A Mg alloy during dry sliding wear was investigated. Wear tests were carried out at 50, 100, 150, 200, and 250 °C using a standard load of 10 N and a sliding distance of 1000 m. Weight loss method was used to calculate the wear rate. Optical microscopy was used to examine the microstructure of the EV31A alloy. FE-SEM with EDS analysis was used to investigate the wear morphology, and XRD analysis was performed both before and after the wear test. A high wear coefficient (K) value (more than 10 −4 ) indicates extreme wear for EV31A in all the scenarios. T4 EV31A had a maximum wear rate of 20.2 mg at 150 °C. The as-cast EV31A alloy exhibits an excellent wear rate at the price of mechanical properties under all test scenarios. Wear resistance is improved by Nd and Zr oxides, although Mg and Gd oxides have little effect. Zn has no effect on the wear behaviour of the EV31A. In as-cast, T4, and T6 heat-treated conditions, the EV31A alloy exhibits delamination (abrasive wear), oxide development (corrosive wear), and delamination mixed with plastic deformation (adhesive wear). A Three-layered ANN and adapted Fine Gaussian SVM predicted tribological characteristics. In ANN prediction, the maximum R 2 was 0.99 for CoF and 0.89 for wear rate, respectively. Despite the fact that the study's normal load is constant, machine learning models allow to deduce that temperature and normal load are the main influential parameters in CoF and wear rate, respectively.
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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".