Fatigue Degradation and Martensitic Transformation of Austenitic Stainless Steel AlSi 321: New Results and Prospects
Bibliographic record
Abstract
On ECRS-6 [1], we have presented first results of the researches of fatigue degradation and martensitic transformation of austenitic stainless steel AISI 321 by neutron diffraction stress analysis. A series of samples preliminary ex-situ cyclically fatigued at the frequency of 5 and 0.5 Hz was in-situ tested on the stress rig of the ENGIN instrument. In the high cycle fatigued (HCF) samples, the applied stress-elastic strain responses of austenite and martensite phases were find out to be strongly different as compared to the low cycle fatigued (LCF) samples, in which they are close. Moreover, the martensite Poisson ratio in the HCF-samples is almost twice to that of observed 0.28-0.30 in austenite and in both phases of the LCF-samples. With the purpose to search the reason of such unusual behavior of the martensite phase, one of the HCF-samples has been anew in-situ tested on the stress rig of the ENGIN-X in: 1) a LCF-mode at the frequency of 0.1 Hz to increase the fatigue level, and 2) a quasistatic mode to measure the applied stress-elastic strain responses of both phases. Also, two of the LCF-samples have been subjected to the ex-situ secondary HCF-testing at the frequency of 5 Hz and again in-situ measured on the ENGIN-X stress rig. Results of the mechanical characterization of phases in the twice fatigued austenitic stainless steel are presented and discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".