Laser cladding of W-Cu composite on bronze substrate
Bibliographic record
Abstract
The feasibility of depositing W-Cu composite overlays on bronze substrate by laser cladding technique was explored. Two types of tungsten powders, with an average size of 10μ and 32μ, were used to deposit coatings with various thicknesses. Metallographic analysis of the composite coatings reveals that, due to the poor wetting behavior, tungsten particles tend to separate from the copper molten pool or coalesce and segregate within the molten pool. The coalescence and separation of the particles occur more easily for the fine powders then for the coarse ones, making it simpler to deposit dense and uniform coatings using coarse tungsten powders. Laser cladding of dense and uniform coatings thicker than 1mm was proven to be difficult, because the separation of tungsten particles from copper substrate occurs after reaching a certain coating thickness, forming a tungsten particles-free copper layer covered with a tungsten particles-rich layer. After these partition layers develop to their critical thickness, localized separation occurs, resulting in a series of tungsten-free copper area surrounded by tungsten particles-rich areas. Nickel addition enhances the possibility of depositing uniform and dense W-Cu composite coatings, preventing or limiting the segregation and separation of constituents. The preliminary results indicate that, by adjusting processing parameters and powder-blend compositions, it is feasible to fabricate dense and uniform W-Cu overlays with various thicknesses that are metallurgically bonded to the substrates.
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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 | 0.000 | 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) | 0.001 | 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 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".