Solid-state cold spray welding: Evaluation and future direction
Bibliographic record
Abstract
Welding is an important manufacturing process for joining complex parts. Despite their widespread use, there exist some drawbacks in the existing liquid-state (e.g., formation of undesirable heat-affected zones around weld area) and solid-state (e.g., high equipment and tooling costs, low production rates, and difficulty in joining intricate geometries) methods. These limitations continually spur the search for new joining processes to improve existing welding methods or develop new ones that circumvent these limitations. To provide a “greener” and low-cost alternative to the traditional welding processes that often produce “soft” heat affected zones (HAZs), we develop and evaluate the expansion of cold spray process—a solid-state high-velocity particle deposition process—to solid-state welding, a process we term “cold spray welding (CSW)”. Using well-defined processing parameters, we cold spray welded (CSWed) AA 6061-T651 plate and compared the results with Tungsten inert gas (TIG) welded counterpart. Although TIG-welded samples exhibit higher tensile properties than CSWed samples (at least based on the CS processing parameters used in this study), our findings show that CSW indeed circumvents major drawbacks in traditional welding processes, including negligible microstructural alterations and the inhibition of deleterious phase transformation and suppression of deleterious HAZ. The examination of CSWed parts reveals low yield strength is connected to ubiquitous microvoids that are formed due to lack of metallurgical bonding; these microvoids act as microcrack initiation sites. We proceed to establish a failure mechanism in the CSWed part to provide direction for future optimization.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".