Micro-milling process improvement using an agile pulse-shaping fiber laser
Bibliographic record
Abstract
We demonstrate the usefulness of INO's pulse-shaping fiber laser platform to rapidly develop complex laser micromachining processes. The versatility of such laser sources allows for straightforward control of the emitting energy envelop on the nanosecond timescale to create multi-amplitude level pulses and/or multi-pulse regimes. The pulses are amplified in an amplifier chain in a MOPA configuration that delivers 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. Also, their pulse-on-demand and pulse-to-pulse shape selection capability at high repetition rates makes those agile laser sources suitable for the implementation of high-throughput complex laser processing. Micro-milling experiments were carried out on two metals, aluminum and stainless steel, having very different thermal properties. For aluminum, our results show that the material removal efficiency depends strongly on the pulse shape, especially near the ablation threshold, and can be maximized to develop efficient laser micro-milling processes. But, the material removal efficiency is not always correlated with a good surface quality. However, the roughness of the milled surface can be improved by removing a few layers of material using another type of pulse shape. The agility of INO's fiber laser enables the implementation of a fast laser process including two steps employing different pulse characteristics for maximizing the material removal rate and obtaining a good surface quality at the same time. A comparison of material removal efficiency with stainless steel, well known to be difficult to mill on the micron scale, is also presented.
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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 | 0.000 | 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".