COMPARISON OF EXPERIMENTALLY MEASURED AND SIMULATED WORKPIECE AND GRINDING WHEEL TOPOGRAPHY USING A NEW DRESSING MODEL
Bibliographic record
Abstract
This paper presents a new empirical model of the dressing process in grinding which is then incorporated into a 3D metal removal computer simulator to numerically predict the ground surface of a workpiece as well as the dressed surface of the grinding wheel. The proposed model superimposes a ductile cutting dressing model with a grain fracture model to numerically generate the resulting grinding wheel topography and workpiece surface. Grinding experiments were carried out using “fine”, “medium” and “coarse” dressing conditions to validate both the predicted wheel topography as well as the workpiece surface finish. For the grinding conditions used in this research, it was observed that the proposed dressing model is able to accurately predict the resulting workpiece surface finish for all dressing conditions tested. Furthermore, similar trends were observed between the predicted and experimentally-measured grinding wheel topographies when plotting the cutting edge density, average cutting edge width and average cutting edge spacing as a function of depth for all dressing conditions tested.
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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 | 0.000 | 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".