Power Ultrasonic Additive Manufacturing: Process Parameters, Microstructure, and Mechanical Properties
Bibliographic record
Abstract
Additive manufacturing (AM) for fabricating 3D metallic parts has recently received considerable attention. Among the emerging AM technologies is ultrasonic additive manufacturing (UAM) or ultrasonic consolidation (UC), which uses ultrasonic vibrations to bond similar or dissimilar materials to produce 3D builds. This technology has several competitive advantages over other AM technologies, which includes fabrication of dissimilar materials and complex shapes, higher deposition rate, and fabrication at lower temperatures, which results in no material transformation during processing. Although UAM process optimization and microstructure have been reported in the literature, there is still lack of standardized and satisfactory understanding of the mechanical properties of UAM builds. This could be attributed to structural defects associated with UAM processing. This article discusses the effects of UAM process parameters on the resulting microstructure and mechanical properties. Special attention is given to hardness, shear strength, tensile strength, fatigue, and creep measurements. Also, pull‐out, push‐out, and push‐pin tests commonly employed to characterize bond quality and strength have been reviewed. Finally, current challenges and drawbacks of the process and potential applications have been addressed.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".