On the Surface Residual Stress Measurement in Magnesium Alloys Using X-Ray Diffraction
Bibliographic record
Abstract
X-ray penetration in magnesium alloys is significant due to the low X-ray mass attenuation coefficient. To measure the surface residual stresses in magnesium alloys, a correction needs to be made to account for penetration depth. The residual stresses in as-received and shot peened AZ31B-H24 rolled sheet samples were measured using two-dimensional X-ray diffraction (2D-XRD) method. The electro-polishing layer removal method was used to find the residual stress pattern at the surface and through the depth. The results show that the corrected residual stresses in a few tens of micrometers layer from the surface differ from the raw stresses. To better estimate the residual stress distribution in the surface, the grazing-incidence X-ray diffraction (GIXD) technique was applied. Additionally, micrographs of the lateral cross-section of the peened specimens confirmed the presence of microcracks in this region, causing the residual stresses to vanish. Due to the low X-ray absorption coefficient of Mg alloys, this study shows how a small uncertainty in a single raw measurement leads to high uncertainty in the corrected residual stresses. The results were corroborated with the hole drilling method of residual stress measurements. The corrected X-ray diffraction (XRD) results are in close agreement with the hole drilling and GIXD results.
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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 | 0.001 |
| 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".