Investigation of the impact of opto-mechanical parameters towards high speed manufacturing of 3-dimensional patterns with nanosecond laser
Bibliographic record
Abstract
Laser-based subtractive manufacturing absorbs enormous attention in the manufacturing of the most advanced materials. The technique has a wide range of applications in biomedicine, photonics and semiconductors and aerospace [1]. Using this technology enhances precision, machining speed, and quality consistency compared to conventional methods. A pulsed laser with high-repetition-rate scans the desired coordinates to offer high-speed manufacturing [1]. However, the machined area has uniform depth (i.e. 2-dimensional micromachining) since the power density does not exceed the ablation threshold out of the focal plane.In order to machine a 3D pattern, we must alter the depth of machining. The variation of machining depth could be achieved by either moving the sample in Z direction or varying the opto-mechanical parameters. Regardless of the approaches, it is essential to quantify the impact of the opto-mechanical parameters. For this purpose, we developed the setup (figure 1) which includes a pulsed laser (wavelength: 532 nm, pulse duration <; 20ns), XY galvo mirror systems and l50 mm converging lens. To obtain a high speed of machining, we decided to move the laser beam by using galvo mirror systems. We developed an algorithm in MATLAB to control the galvo for the laser beam delivery into the desired coordinates. In the current study, we conducted 3 sets of experiments to investigate the effects of the opto-mechanical parameters on Magnesium. In the first set of experiments, we studied the impacts of the pulse overlap (0, 40 and 80 %) and the laser power (1.1 and 2.4 w) on machining depth. For the second set of experiments, we kept the overlap to 80% and varied the number of pulses from 50 to 400 for 1.1 and 2.4 w laser power. For the last set of experiments, we utilized the image-based approach [1], [2] to machine the 2D complex pattern (figure 2 (b)) by applying the best combination of optical parameters. After machining, the specimens were scanned by the interferometer (WYKO NT110) to measure the depth and roughness of the samples. The results depict the growth of laser power (from 1.1 to 2.4 w) significantly increases the depth of the machined zone from 4 to 24 μm. We observed increasing the pulse overlap from 0 to 80 % drops the surface roughness. Increasing the number of pulses in specific XY coordinates from 50 to 400 could rise the depth by 4 times (figure 2 (a)). In this study, we realized machined depths could be varied by changing the laser power, pulse overlap, and the number of pulses. We also developed a way to machine a 2D/3D complex pattern with minimum achievable roughness. This approach can be an effective step toward the subtractive manufacturing of 3D-constructs.
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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 | 0.000 | 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".