Automatic quality monitoring of two-photon printed devices based on deep learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Two-photon 3D printing technology is an additive manufacturing technology that uses the two-photon absorption process of near-infrared radiation to create a three-dimensional micro-nano scale structure with extremely high resolution. However, in the preparation process of two-photon printing, the laser parameters for inducing photopolymerization have a huge impact on the quality of the polymer structure. Therefore, monitoring the quality of the device during the manufacturing process and rationally optimizing the laser parameters are of great significance to the field of additive manufacturing. In this study, we collected video data of different structural devices prepared by self-made photoresist materials under different laser parameters, and used a variety of convolutional neural network variant models to train and verify our collected datasets. The results show that the variant deep learning neural network model can classify the quality of polymer structures in milliseconds, and the test accuracy can reach 95%.
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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 | 0.000 |
| 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 it