Effect of multi-parameter optimization of water-laser coupling device and nozzle geometry on the stability of water-guided laser beam
Bibliographic record
Abstract
The stability of the water jet flow used in water-guided laser processing plays a crucial role in determining the quality of the processing process. The present study aims to investigate the effect of water-laser coupling device and nozzle geometry on the stable length of a water-guided laser beam. A numerical model is developed to analyze the internal flow field within the coupling device. Computational fluid dynamics (CFD) simulations are employed to examine how variations in cone nozzle's length-to-diameter ratio, divergence angle, nozzle aperture, and inlet pressure that affect the flow characteristics of water jet. Subsequently, the Latin Hypercube experimental design method is employed to establish parameter samples and construct a Kriging approximation model for the stable length of water-guided laser beam. The Multiple Island Genetic Algorithm (MIGA) is utilized for global optimization of the approximation model, while CFD methods are employed to analyze and validate the optimization results. Finally, experimental verification was conducted to determine the stable length of water-guided laser beam generated by the optimized cone nozzle structure. The research findings demonstrate that the optimized nozzle structure can compensate for approximately 21 mm in the stable distance of water-guided laser beam under the pressure of 5.0 MPa. This study provides valuable guidance for enhancing the performance and engineering applications of laser micro-jet processing technology.
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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 | 0.001 | 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 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".