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Record W3065501862 · doi:10.1109/pn50013.2020.9167010

Investigation of the impact of opto-mechanical parameters towards high speed manufacturing of 3-dimensional patterns with nanosecond laser

2020· article· en· W3065501862 on OpenAlexaff
Shayan Mohammadi Pour khajani, Hamid Ebrahimi Orimi, Sivakumar Narayanswamy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsHôpital Maisonneuve-RosemontConcordia University
Fundersnot available
KeywordsMachiningLaserSurface micromachiningMaterials scienceOpticsLaser beam machiningLaser power scalingLaser ablationPulse durationPhotonicsLaser beam qualityComputer scienceMechanical engineeringOptoelectronicsPhysicsFabricationLaser beamsEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.216
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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