Framework for the Monitoring of Complex Surfaces Based on Optical Assessment
Bibliographic record
Abstract
The optical perception of surfaces manufactured with high precision is an important quality feature for most products.The respective manufacturing process is rather complex and depends on a variety of process parameters which have a direct impact on the surface shape and topography in the most cases.Surface-shapes, topographies and colorings are mostly measured using classical methods (roughness measuring device, gloss measuring device, spectrophotometer, computer tomography, or tactile coordinate measuring instruments).To improve the conventional methods of condition monitoring, in this case represented by the monitoring of the surface, a new image processing approach is needed to get a faster and more cost-effective analysis of manufactured surfaces.For this reason, different optical techniques based on images have been developed over the past years.In this paper, a framework for surface monitoring is outlined and discussed in detail according to every single step along the monitoring process.For this purpose, the study differentiates between the application of the descriptive statistics as well as the application of artificial intelligence.Both applications are mainly based on the same data sources, though on different sample sizes and provide answers to differing questions that often complement each other.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".