Optimum end milling tool path and machining parameters for micro Laval nozzle manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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) | 0.000 | 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 it