PROCESSES AND TECHNOLOGIES OF COLD GAS DYNAMIC SPRAYING OF AGRICULTURAL EQUIPMENT
Bibliographic record
Abstract
The article is devoted to the technology of cold gas-dynamic spraying - a relatively new technology for creating functional coatings in which narrowed-expanding nozzles (De Laval nozzles) are used, in which solid powders are sprayed at or close to supersonic speed in the direction of the substrate, where they plastically deform and adhere. Cold gas dynamic spraying is an innovative process that has received widespread scientific interest and industrial applications in the automotive, aerospace and biotechnology industries in recent years. Spraying various powder materials in this way provides protection against corrosion and can increase the mechanical strength and wear resistance of details. With that method it is possible to create functional coatings with different thermal conductivities and electrical conductivities, which can be used as insulation materials, conducting and isolation surface layers of parts, coatings for controlling gaps, restoring and repairing details. The article discusses modern developments in the field of experimental analysis of technologies and processes of cold gas-dynamic spraying, the systems of cold gas-dynamic spraying and coating formation are determined and the parameters and principles of the process are described. The installation for spraying surfaces developed at VNAU is presented and researches and the analysis of possibility of use of a method of a cold gas-dynamic spraying for restoration of details of autotractor equipment of agricultural purpose are carried out.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".