Characteristics of pulsetrain-burst machining
Bibliographic record
Abstract
Summary form only given, as follows. The discovery of ultrafast-laser pulsetrain-burst machining at the University of Toronto has prompted new research into the physics involved in this regime of laser-matter interactions. Using the oscillator output of the Toronto FCM-CPA laser system we have been able to explore the effects of machining with a train of up to 400 1.2 ps pulses at a repetition rate of 133MHz. Pulsetrain burst machining has advantages over single pulse and CW laser machining. These include that drilling of holes at fluencies that would cause cracking or other negative thermal effects seen when using the other sources. Since the original work on this topic, which included the characterization of morphology of the drilled holes, we have further examined the physics behind this and conventional ultrafast laser machining by varying different parameters of the micromachining system. The technique is particularly useful for laser-material processing of brittle materials, especially glasses. We will describe the optical and thermal mechanisms behind hole-depth saturation in metals; part of which we believe is due to a loss of coherence caused by the pulse propagating down a multimode wave guide. We also will describe the connection between these mechanisms and our measurements of differential etch-rates.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.009 | 0.002 |
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".