Laser Light-Based Technique for Detection and Assessment of Localized Defects in Reflective Automotive Surfaces
Bibliographic record
Abstract
Surface quality plays an important role on aesthetic appeal of any exterior component of a vehicle. As such, smooth and defect-free are some of the critical characteristics of an automotive surface. Numerous manufacturing-related factors such as production environment, substrate material, coating material, handling is known to generate defects to inherently decrease the overall quality of the external surface. Many of these defects tend to be localized while spreading over large areas of the surface. However, the vast majority of available systems are unable to unequivocally quantify surface defects, and, in most cases, the surface quality assessment is performed in a relative and rather qualitative manner. To address this, the main goal of the present study was to develop a new laser light-based technique capable to detect and quantify the localized defects that are present on the surface of reflective components for automotive exteriors. For this purpose, the setup of the proposed prototypical system was validated through a blend of optical simulations and experiments performed on concave and convex defects with determined geometries and with sizes placed in the sub-millimeter range. This type of knowledge is essential in understanding how the collimated fascicle of light is being redirected after interacting with localized surface defects. Future extensions of this work will target the more general case of globally spread defects of an undetermined shape that are - for instance - characteristic to painted automotive bodies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".