Automatic real-time guidance of laser machining with inline coherent imaging
Bibliographic record
Abstract
Optical coherence imaging can measure hole depth in real-time (> 20 kHz) during laser drilling without being blinded by intense machining light or incoherent plasma emissions. Rapid measurement of etch rate and stochastic melt relaxation makes these images useful for process development and quality control in a variety of materials including metals, semiconductors and dielectrics. The ability to image through the ablation crater in materials transparent to imaging light allows the guidance of blind hole cutting even with limited a priori knowledge of the sample.Significant improvement in hole depth accuracy with the application of manual feedback from this imaging has been previously demonstrated [1]. However, the large quantity of raw data and computing overhead are obstacles for the application of coherent imaging as a truly automatic feedback mechanism. Additionally, the high performance components of coherent imaging systems designed for its traditional application in biological imaging are costly and may be unnecessary for materials processing. In this work, we present a coherent imaging system design that costs less than a fifth of comparable commercial products. We also demonstrate streamlined image processing suited for automated feedback that increases processing speed by two orders of magnitude.
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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 | 0.000 | 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 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".