Study on the Role of Casting Defects and Surface Finish on Fatigue Life in Die-cast AM60B Magnesium Alloy Using X-Ray Computed Tomography
Bibliographic record
Abstract
Abstract Magnesium alloy has attracted considerable attention from the automotive industry because of its low density and high specific strength combined with excellent casting capabilities. However, the role of casting defects and surface finish on the fatigue life emerges as a major concern. To study the role of surface finish, the die-cast skin was removed from the selected test specimens by hand polishing. No major difference in surface roughness was observed between the die-cast and polished surface. To characterize the casting defects in each test specimen, prior to the fatigue test, X-ray computed tomography (XCT) scans were performed on a desktop micro-XCT scanner. The preliminary XCT scans revealed a large number of gas and shrinkage pores concentrated in the center sections of the test specimens and a few scattered close to the free surface. In addition to gas and shrinkage pores, in unpolished specimens, small microcracks and notch-like surface imperfections were also observed on the edge of the test specimens. Strain controlled fatigue tests were performed on polished and unpolished test specimens at a 0.3 % strain amplitude. After every 2,000 fatigue cycles, the test was interrupted and XCT was performed to study the crack initiation and propagation in the respective test specimens. The fatigue test exhibited no major difference in fatigue life between the unpolished and polished specimens. However, XCT scans revealed the dominant fatigue crack initiated from the surface imperfections in unpolished specimens and from casting pores closer to the free surface in polished specimens. The fatigue test results and spatial distribution of casting pores were compared with the specimens extracted from a die-cast shock-tower structure from a previous study. Although a difference in spatial distribution of the casting pores was observed, it had no major impact on the fatigue life between the two sets of specimens.
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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 | 0.000 |
| 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 | 0.000 |
| 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".