Material micromachining using a pulsed fiber laser platform with fine temporal nanosecond pulse shaping capability
Bibliographic record
Abstract
We report on recent advances in laser material processing using a novel pulsed fiber laser platform providing pulse shape agility at the nanosecond time scale and at high repetition rates. The pulse shapes can be programmed with a time resolution of 2.5 ns and with an amplitude resolution of 10 bits. Depending on the desired laser performances, the pulses are generated either by directly modulating the drive current of a seed laser diode or by modulating the output of a seed laser diode operated in CW with electro-optic modulators. The pulses are amplified in an amplifier chain in a MOPA configuration. Advanced polarization maintaining LMA fiber designs enable output energy per pulse up to 60 μJ at 1064 nm at a repetition rate of 200 kHz with excellent beam quality (M2< 1.1) and narrow line widths suitable for efficient frequency conversion. Micro-milling experiments were carried out with stainless steel, in which processing microstructures of a few tens of microns in size usually represents a challenge, and aluminum, whose thermal conductivity is about 20 times higher than stainless steel. The results obtained with two metals having very different thermal properties using different pulse shapes with durations varying between 3 ns and 80 ns demonstrate the benefits of using lasers offering flexible pulse durations and controllable pulse intensity profiles for rapidly optimizing a process in different applications while using the same laser with respect to conventional methods based on pulsed laser with fixed pulse shapes. Numerous applications are envisioned in a near future, like the micromachining of multi-layered structures, in particular when working with the harmonics of the laser.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".