Separation of surface hardened glass with non-ablation laser technique
Bibliographic record
Abstract
The study investigated cutting of glass by non-laser ablation technique through non-linear absorption laser pulses induced optical breakdown, melting and plasma expansion throughout the glass thickness from bottom to top. Picosecond near-infrared laser pulses were used. The laser beam was focused with an objective lens with numerical aperture (NA) of 0.1. The study revealed that focus position is a key factor in determining glass well-separation. When the laser focus was placed at 500 µm below the top surface for a 700 µm thick ion exchanged Gorilla glass, namely more than half of the glass thickness, the glass could be well-separated into two pieces. At focus near the top surface, V-shaped ablation grooves were generated at the glass top surface without glass separation. At focus inside the glass and near to the bottom surface, internal scribing occurred at the bottom part of the glass. The glass could also be separated by scribing-caused cracking throughout the glass entire thickness. At the optimal focus ranges, well-separation of the glass was found to be at speeds of 0.5-6 mm/s and pulse frequency around 200 KHz with laser laser fuence of 0.87 J/cm2. At low pulse frequencies such as below 100 KHz, glass top surface was ablated without glass separation. At higher pulse frequencies above 300 KHz, cracks were produced and the glass was separated into multiple pieces. Interestingly, at pulse frequency upto 500 KHz, both top surface ablation and bottom surface ablation occurred. Eventually, the glass was cracked into multiple pieces. Different pulse frequency produces different pulse energy. For example, 200 KHz generates a laser fluence of 0.87 J/cm2 at the glass top surface, 100 KHz for 1.59 J/cm2 and 300 KHz for 0.60 J/cm2 etc. Furthermore, the glass was cracked at the high speeds above 10 mm/s. The results indicate that there is an optimal time-dependent energy deposition, namely, laser energy deposition rate for glass well-separation. The calculation shows that the energy deposition rates were between 1.29×104 µJ/μm3s to 1.54×105 µJ/μm3s.
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 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 | 0.000 | 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) | 0.000 | 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".