Prediction of Grinding Force Distribution in Wheel and Workpiece Contact Zone
Bibliographic record
Abstract
A novel method is reported for predicting the distribution of normal and tangential grinding forces in wheel and workpiece contact zone or along their contact arc. This work was motivated by the need to obtain the maximum force acting on individual active abrasive grains for establishing the probability of grain fracture and pullouts due to this force. Horizontal and vertical forces measured in the transient cut-in or cut-out stage of a grinding pass are utilized in this method to predict the horizontal and vertical forces acting on each portion of the contact arc. And then these forces are subsequently converted to tangential and normal forces per unit length along the arc to obtain the force distribution. To illustrate the application of this method, forces measured in the transient cut-out stage in the grinding of tungsten carbide with electroplated diamond wheels were employed to predict the force distribution, which was further applied to predicting the transient grinding power at the cut-in and cut-out stages. The predicted power was found to match very well with the measured power.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".