Reduction of stress cracking in micromachining of glass by preheating of samples
Bibliographic record
Abstract
Laser micromachining may be used for a variety of applications including drilling holes or creating trenches in dielectric materials. Cracking around the ablated features can be a significant problem for many applications, particularly when micromachining glass. One possible method for crack reduction, investigated here, involves heating of the substrate during ablation. This leads to a more ductile material that is more able to withstand the thermal shock of the ablation process. In order to increase the ductility, the glass targets are heated by physical contact with an electric heating element. The results of micromachining are analyzed using an optical microscope. The amount of cracking is quantified in terms of the number of visible radial cracks. For nanosecond micromachining, a reduction in the number of cracks and an improvement in the quality of the holes are observed as the glass is heated. The relative improvement using heated substrates and nanosecond pulses is also compared to femtosecond ablation of room temperature substrates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".