International Journal of Fatigue, Volume 54:Evaluating surface deformation and near surface strain hardening resulting from shot peening a tempered martensitic steel and application to low cycle fatigue
Bibliographic record
Abstract
The plastic deformation resulting from shot peening treatments applied to the ferritic heat resistant steel FV448 has been investigated. Two important effects have been quantified: surface roughness and strain hardening. 2D and 3D tactile and optical techniques for determining surface roughness amplitude parameters have been investigated; it was found that whilst Ra and Sa were consistent, Sz was generally higher than Rz due to the increased probability of finding the worst case surface feature. Three different methods for evaluating the plastic strain profile have been evaluated with a view to establishing the variation in yield strength near the surface of a shot peened component. Microhardness, X-ray diffraction (XRD) line broadening and electron backscatter diffraction (EBSD) local misorientation techniques were applied to both uniaxially deformed calibration samples of known plastic strain and samples shot peened at intensities varying from 4A to 18A to establish the variation in plastic strain and hence the variation in yield strength. The results from the three methods were compared; XRD and EBSD profiles were found to be the most similar with microhardness profiles extending much deeper into the sample. Changes in the measured plastic strain profile after exposure to low cycle fatigue and the correlation of these changes with the cyclic stress–strain behaviour of the material are also discussed with a view to assessing the importance of the dislocation profile in component life assessment procedures. 2013 Elsevier Ltd. All rights reserved.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".