Ultrasonic pulsed waterjet peening of Ti-6Al-4 V manufactured by laser powder bed fusion
Bibliographic record
Abstract
Laser powder bed fusion (L-PBF) processed metal components invariably possess poor surface quality in their as-built state, along with high tensile residual stresses. In order to optimize the surface properties and mitigate the adverse effects of L-PBF fabrication, the implementation of an appropriate post-processing surface treatment is essential. In the present study, Ti-6Al-4 V samples were fabricated by the L-PBF method, and showed an initial surface roughness, Ra, of ∼17 μm with pronounced anisotropic tensile residual stresses. Ultrasonic pulsed waterjet (UPWJ) was subsequently applied as a novel and environmentally benign surface peening technique for the L-PBF Ti-6Al-4 V parts. Samples were UPWJ peened either in the as-built state or following machining, through either a milling or grinding step applied to the L-PBF samples. It is demonstrated that the post-peening roughness could be reduced to Ra ≤1 μm through surface machining following by UWPJ peening, which was accompanied by the introduction of substantial compressive residual stresses up to approximately −800 MPa. Machining of the L-PBF parts, followed by UPWJ peening, led to a significant enhancement in scratch hardness (HS p ), exhibiting a maximum improvement of approximately 23 %. The combined use of machining followed by UWPJ peening is demonstrated to be a practical strategy to achieve both enhanced surface properties and high compressive residual stress for post-processing of L-PBF fabricated Ti-6Al-4 V parts. • As-built PBF-LB/Ti6Al4V had high tensile residual stress and surface roughness • UPWJ peening was an effective post-processing method for PBF-LB/Ti6Al4V • Milling is recommended for surface preparation of the parts before UPWJ peening • Milling and UPWJ peening induced high compressive residual stress (−800 MPa) • A maximum improvement of 20 % in scratch hardness was achieved after UPWJ peening
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".