Comparative Analysis of the Applicability of Feed-Forward and Recurrent Neural Networks for Prediction of Surface Quality of Laser Polished Surfaces
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
Laser polishing (LP) is an emerging manufacturing process capable to address some of the significant limitations of traditional surface quality improvement technologies such as abrasive or chemical based polishing processes. By reconfiguring the topography of the outer surface, surface characteristics such as quality, aesthetics, wettability, friction, bio-fouling resistance, and others can be enhanced at a relatively low cost. However, numerous LP process parameters have to be fine-tuned to achieve the intended surface quality. This makes the selection of optimal process parameters time consuming and often unrepeatable. This study suggests that while both feed-forward and recurrent neural networks can be used to predict LP surface quality with a reasonable accuracy, the latter is characterized by a superior performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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