Effects of Multiaxis Oscillations: A Novel Approach to Residual Stress Measurements via X-Ray Diffraction
Bibliographic record
Abstract
Abstract X-Ray Diffraction (XRD) has been used for the measurement of residual stress (RS) since the 1920s, with numerous advancements in accuracy, precision, and speed since. XRD methods rely on the queried material to be quasi-isotropic, homogeneous, and to have a near-random grain orientation distribution; however, often this is not the case. When the coherent domain size (or grain size) is large relative to the irradiated volume, a non-random grain orientation distribution may be sampled, and the diffraction Debye ring may become "spotty." The presence of a spotty Debye ring introduces errors in the apparent diffraction peak position and, by extension, results in errors in the determination of RS. Traditionally, oscillations have been conducted in the same plane as the tilting plane in either Omega (Iso-inclination) or Chi (Side-inclination) modes to minimize experimental errors. However, there is no existing literature on Omega oscillations while measuring RS in Chi mode, or vice versa, or the use of simultaneous oscillations in both planes during measurements. In this novel study, experimental data were collected in both modes, with oscillations both individually and simultaneously, in both axes over a range of oscillation angles. Empirical predictive modeling was employed to compare with experimental data with the goal of optimizing RS measurement accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".