Optical Surface Analysis with Support Vector Machines based on Two Different Measurement Techniques
Bibliographic record
Abstract
The surface topography as well as the optical perception are important features for evaluating the quality of fine grinded knives.Parameters as the surface roughness, gloss or coloring are used for the quantification of these features.The measuring is implemented by the use of traditional methods, which are manual, time-consuming and cost-intensive.On top of that, the application of these methods for the condition monitoring of the ongoing process is rather limited.Therefore, a new, faster and more cost-effective approach is needed to improve the classical measurement methods.A conceivable approach could be based on image analysis.Over the past years, different contactless image analysis based approaches have been developed to simplify the traditional roughness measurement methods.Some studies propose picture pre-processing and feature extraction in combination with machine learning algorithms.The overall goal of the presented research activities is the development of a condition monitoring tool which can be implemented in the ongoing grinding process of the knives.It should be used to ensure the knives quality and to reduce rejects by an immediate detection of deviations of the target values and the possibility to adapt the production process accordingly.For this reason, a data set based on cutlery samples has been generated and analyzed.The extraction of features of the data set is presented for a better understanding of the training process.The features are used to train various machine learning algorithms with and without a combination of logged process parameters to evaluate the surface roughness.Within this study the image of each grinded surface is analyzed regarding its measured arithmetic average roughness value (Ra) by the use of Support Vector Machine (SVM) and Support Vector Regressor (SVR) algorithms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".