Effect of Repetition Rate on Femtosecond Laser-Induced Homogenous Microstructures
Bibliographic record
Abstract
We report on the effect of repetition rate on the formation and surface texture of the laser induced homogenous microstructures. Different microstructures were micromachined on copper (Cu) and titanium (Ti) using femtosecond pulses at 1 and 10 kHz. We studied the effect of the repetition rate on structure formation by comparing the threshold accumulated pulse ( F Σ p u l s e ) values and the effect on the surface texture through lacunarity analysis. Machining both metals at low F Σ p u l s e resulted in microstructures with higher lacunarity at 10 kHz compared to 1 kHz. On increasing F Σ p u l s e , the microstructures showed higher lacunarity at 1 kHz. The effect of the repetition rate on the threshold F Σ p u l s e values were, however, considerably different on the two metals. With an increase in repetition rate, we observed a decrease in the threshold F Σ p u l s e on Cu, while on Ti we observed an increase. These differences were successfully allied to the respective material characteristics and the resulting melt dynamics. While machining Ti at 10 kHz, the melt layer induced by one laser pulse persists until the next pulse arrives, acting as a dielectric for the subsequent pulse, thereby increasing F Σ p u l s e . However, on Cu, the melt layer quickly resolidifies and no such dielectric like phase is observed. Our study contributes to the current knowledge on the effect of the repetition rate as an irradiation parameter.
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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".