Optimization of Cold Spray Nozzles Based on the Response Surface Methodology
Bibliographic record
Abstract
Spraying technical parameters are important factors that affect spraying efficiency. Most studies on spraying technical parameters use single-factor methods to study the speed of spray particles, and few scholars have studied the joint influence of multiple factors. This article uses gas temperature, particle size, and gas pressure as independent variables, and the independent variables interact. The design-expert method was used to establish a linear regression equation model of the velocity of sprayed Al and Cu particles at the Laval exit and the velocity before deposition with the substrate, and the response surface analysis method was used to predict the optimal spraying parameters of Al and Cu particles. The study found the contribution rate of three factors to particle velocity: the prediction of particle velocity at the exit of the Laval nozzle and before deposition with the substrate was realized; the error between the predicted value of particle velocity and the actual value obtained by simulation is less than 1.6 %, indicating that the speed linear regression equation established is effective and reliable in predicting the simulation results; the optimal spraying parameters and particle speeds of Al and Cu particles were obtained through response surface analysis.
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 | 0.003 | 0.001 |
| 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 | 0.000 |
| 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".