Laser Assisted Cold Spray for Improved Adhesion of Soft Materials to Hard Substrates—Case Study for Copper Coatings on Steel
Bibliographic record
Abstract
Abstract In cold spray, high adhesion of soft materials on hard substrates is commonly achieved by using helium as the propelling gas. This is the case of copper coatings on steel where adhesion may reach values as high as 60 to 80 MPa (glue failure), however, helium is a limited, expensive natural resource, and the use of more abundant nitrogen gas is preferred in an industrial setting. Unfortunately, when using nitrogen gas, little to no adhesion is obtained. In order to eliminate the use of helium gas we studied how laser assisted cold spray could lead to an improvement in adhesion of nitrogen sprayed copper coatings. In this work, several laser parameters (e.g., power and spot size) and process parameters (traverse speed, relative position laser spot vs. gas jet) were varied at a coupon level. Upon optimization, an equivalent adhesion to the coatings prepared with helium was obtained. Furthermore, the cross section of the coatings showed that the copper particles penetrated the steel, similar to what is observed when using helium gas. Optimization of these parameters for application to large diameter (~559 mm) cylinders was also performed. A discussion on the mechanisms which contribute to achieving high adhesion considering the use of helium versus laser assistance is provided.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".