METHODOLOGY OF THE ASSESSMENT OF THE ABRASIVE TOOL’S ACTIVE SURFACE USING LASER SCATTEROMETRY
Bibliographic record
Abstract
Diagnostics of abrasive tools requires the use of modern measurement techniques which allows for fast assessment of a surface in order to determine, for example, the degree of its wear or to detect various type of defects. A wide group of optical measurement methods used for this type of assessment are based on the phenomenon of light scattering. One such method based on imaging and analysis of light scattering from a surface is laser scatterometry. In this paper, by utilizing laser scatterometry supported by image analysis techniques, a proposal for a methodology of assessment of the degree to which smearing of the grinding wheel active surface (GWAS) occurs during the plunge grinding process, was presented and discussed. Select results of experimental investigations carried out on bearing steel 100Cr6 were also presented. The obtained results confirmed the efficacy of the above-mentioned techniques that could be an interesting alternative to other methods already used in such measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".