Surface Roughness Estimation of Turned Parts from Optical Image Measurements and Wavelet Decomposition
Bibliographic record
Abstract
The surface roughness is very significant information required for product quality on the field of mechanical engineering and manufacturing, especially in aeronautic. Its measurement must therefore be conducted with care. In this work, a measuring method of the surface roughness based on machine vision was studied. The authors' use algorithms to evaluate new discriminatory features thereby than the statistical characteristics using the coefficients of the wavelet transform and used to estimate the roughness parameters. This vision system allows measuring simultaneously several parameters of the roughness at the same time, order to meet for the desired surface function used. The results were validated on three different families of materials: aluminum, cast iron and brass. The impact of material on the quality of the results was analyzed, leading to the development of multi-materials. The study had shown that several roughness parameters can be estimated using only features extracted from the image and a neural network without a priori knowledge of the machining parameters.
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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 | 0.001 | 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 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".