Effects of Ultrasonic Impact Treatment on the Stress-Controlled Fatigue Performance of Additively Manufactured DMLS Ti-6Al-4V Alloy
Bibliographic record
Abstract
Additive manufacturing (AM) offers many advantages for the mechanical design of metal components. However, the benefits of AM are offset to a certain extent by the poor surface finish and high residual stresses resulting from the printing process, which consequently compromise the mechanical properties of the parts, particularly their fatigue performance. Ultrasonic impact treatment (UIT) is a surface modification process which is often used to increase the fatigue life of welds in ship hulls and steel bridges. This paper studies the effect of UIT on the fatigue life of Ti-6Al-4V manufactured by Direct Metal Laser Sintering (DMLS). The surface properties before and after the UIT are characterized by surface porosity, roughness, hardness and residual stresses. Results show that UIT enhances the fatigue life of DMLS Ti-6Al-4V parts by suppressing the surface defects originating from the DMLS process and inducing compressive residual stresses at the surface. At the adopted UIT application parameters, the treatment improved the fatigue performance by 200%, significantly decreased surface porosity, reduced the surface roughness by 69%, and imposed a compressive hydrostatic stress of 1644 MPa at the surface.
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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".