Application of X-Ray Diffraction for Residual Stress Determination in Mechanical Components
Bibliographic record
Abstract
X-ray diffraction has been applied to the measurement of residual stresses in mechanical and structural components for decades. It has been applied non-destructively in the laboratory on small and large components as well as in the field on very large components and structures. The technique is well suited to a wide variety of applications. Some examples include: process development and evaluation (including machining, heat treatment, welding and assembly), quality control, screening of non-conforming components and verifying the health of components during service at different intervals and at the end of life. Both residual stress levels and the associated work-hardening generated can be characterized using the shift and the broadening of the x-ray diffraction peak respectively. Since x-ray diffraction uses the atomic lattice spacing (d-spacing) as a strain gage it is thus only applicable to crystalline materials. In this paper, examples of x-ray diffraction applications on different materials subjected to various processes are illustrated. The effect of residual stresses on the fatigue lifetime of components is also considered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".