Effect of the Rheocasting Process and of the SLS Layer on the Fatigue Behavior of 357 Aluminum Alloy
Bibliographic record
Abstract
Two objectives were targeted: 1) compare the high cycle fatigue behavior of rheocast aluminum alloy 357 prepared by the swirl enthalpy equilibration device (SEED) and by the Council for Scientific and Industrial Research (CSIR) process, and 2) study the effect of surface liquid segregation (SLS) on the fatigue behavior of the CSIR material. Rectangular hourglass specimens machined from rheocast plates were tested at four stress amplitudes in axial fatigue with a stress ratio of R = -1 and a frequency of 20 Hz. Results obtained for SLS free specimens show that the SEED and the CSIR processes produce rheocast materials with comparable high cycle fatigue properties, 115 MPa at 107 cycles. In order to study the influence of surface liquid segregation, slightly polished specimens with a remaining SLS of nearly 750 microns thick were also tested. According to the results, the SLS reduces the average fatigue strength by approximately 5% (110 MPa vs. 115 MPa at 107 cycles). For SLS free specimens, the fatigue crack initiated at shrinkage cavities, oxide films or in the alpha globules. On the other hand, for specimens with SLS, no crack initiation in the alpha globules was observed. The main crack initiation mechanism was identified to be a deformation incompatibility between regions characterized by higher silicon content compared to nominal eutectic regions. The originality of the work is provided by the rigorous comparative analysis of the fatigue performance of components produced in two different rheocasting facilities, but tested in a single laboratory. It is also the first fundamental research published on the mechanical effect of surface liquid segregation. It confirms that SLS should be removed in critical areas in order to optimize the fatigue resistance of rheocast components.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".