Fatigue Improvement of Additive Manufactured Ti–TiB Material through Shot Peening
Bibliographic record
Abstract
In this work, fatigue improvement through shot peening of an additive manufactured Ti–TiB block produced through Plasma Transferred Arc Solid Free-Form Fabrication (PTA-SFFF) was investigated. The microstructure and composition were explored through analytical microscopy techniques such as scanning and transmission electron microscopy (SEM, TEM) and electron backscatter diffraction (EBSD). To investigate the isotropic behavior within the additive manufactured Ti–TiB blocks, tensile tests were conducted in longitudinal, diagonal, and lateral directions. A consistent tensile behavior was observed for all the directions, highlighting a nearly isotropic behavior within samples. Shot peening was introduced as a postmanufacturing treatment to enhance the mechanical properties of AM specimens. Shot peening led to a localized increase in hardness at the near-surface where stress-induced twins are noted within the affected microstructure. The RBF-200 HT rotating-beam fatigue machine was utilized to conduct fatigue testing on untreated and shot-peened samples, starting at approximately 1/2 the ultimate tensile strength of the bulk material and testing within low- (<105 cycles) to high-cycle (>105 cycles) regimes. Shot-peened samples experienced significant improvement in fatigue life, increasing the fitted endurance limit from 247.8 MPa for the untreated samples to 318.3 MPa, leading to an increase in fatigue resistance of approximately 28%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".