Mechanism study on microformability of optical glass in ultrasonic‐assisted molding process
Bibliographic record
Abstract
Abstract Ultrasonic‐assisted glass molding process ( UGMP ) has gained a promising start in microptics fabrication in recent years. To further understand the microforming process and comprehensively evaluate the microformability of the glass in UGMP , theoretical considerations and numerical simulations are performed in this study. A generalized dynamic viscoelastic model of glass in UGMP is first reformulated based on the ultrasonic thermo‐mechanical effects. Correspondingly, a multiphysics numerical model of UGMP is developed to dynamically observe the thermo‐mechanical rheological behaviors of the L‐ BAL 42 glass inside the microscale mold cavities. The stress distributions and deformation features of the formed micro‐V‐grooves in different molding processes are further employed to fully investigate the ultrasonic mechanical and thermal effects on glass molding. The results show that, as a product of the combined ultrasonic mechanical and thermal effects, the filling rate of the glass in UGMP is observably increased and homogenized, while its maximum forming stress is reduced by 57.6% compared with the conventional molding process ( GMP ). It is also found that, the ultrasonic thermal effect is dominant in lowering the forming stress of the glass, while the ultrasonic mechanical effect plays a leading role in homogenizing the filling rate of the glass. This study will provide both theoretical and technical supports for high‐efficiency and high‐precision fabrication of surface‐relief microptical elements.
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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".