Optimum end milling tool path and machining parameters for micro Laval nozzle manufacturing
Bibliographic record
Abstract
Cutting tool path has significant effects on the performance of micro nozzles manufactured by micro machining. Different tool paths induced different directions of surface roughness. As for it, the manufacturers need to obtain optimal cutting tool path and cutting parameters. In this article, optimum machining parameters for the fabrication of micro Laval nozzle with two different end milling tool paths are presented. First, surface roughness models for different types of cutting tool paths are proposed. A case of machined nozzle surface is then given to verify the applicability of the developed roughness model. Second, theoretical profile geometries for the Laval nozzle to be manufactured are designed. Third, the influences of surface roughness on the nozzle performance parameters including total pressure, average outlet velocity and thrust are investigated through computational fluid dynamic analysis. Simulated performance parameters are contrasted with their theoretical values. It is found that for different tool paths, the nozzle of axial tool path has larger total pressure and average outlet velocity than that of circular tool path. Moreover, with surface roughness increasing, thrust decreases obviously when surface roughness R z is larger than 4.8 μm. Micro end milling experiments based on axial tool path are then performed, and the optimum cutting parameters are obtained. Finally, a nozzle was manufactured with the axial tool path as well as the optimized cutting parameters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".