Characterization of Residual Stresses on Steel Coil Springs via X-Ray Diffraction Techniques
Bibliographic record
Abstract
It is well known that manufacturing operations produce material conditions that can either enhance or debit the fatigue life of production components. One of the most critical aspects of material condition that can have a significant impact on fatigue life is residual stress (RS) [1, 2]. When springs are manufactured, the spring stock may undergo several operations during production. Additional operations may also be introduced for the purpose of imparting the spring with beneficial surface RS to extend its fatigue life and increase its ability to execute the task it was designed to perform. The resultant RS present in production springs as a result of the various fabrication and processing operations applied can be predicted and modeled, however, RS measurements must be performed in order to quantify the RS state with precision. X-ray diffraction (XRD) techniques can be used to determine the RS state of such components precisely, thus enabling manufacturers the ability to accurately and quantitatively characterize and control the effects of their production processes [3].
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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) | 0.003 | 0.001 |
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".