Fabrication of Sensor-Integrated Parts Using Cold Spray Additive Manufacturing
Bibliographic record
Abstract
Currently used metal additive manufacturing (AM) processes are often limited regarding build rates or build volumes. Cold spray, as a comparably young AM-process, enables the production of large metal components at high deposition rates. The technology, which was developed in the mid-1980s as a coating process, accelerates particles to supersonic speeds using a De-Laval-nozzle. The particles are subsequently sprayed with high impact onto a substrate, which causes the particles to form dense layers by plastic deformation induced by their high kinetic energy. Good processability of ductile materials and manufacturability of multi-material structures thus becomes feasible. Cold spray, which can be seen as an addition to the directed energy deposition technologies, will be the focus and be linked to the insertion of sensors within this work. Possibilities and limitations, of embedding temperature sensors in AM-components during the process, are systematically developed and presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".