Exploration of a New Approach for Calibrating Grinding Power Model
Bibliographic record
Abstract
This paper is concerned with the reliable calibration of the grinding power model. The power model used in the present paper is expressed by formulae with grinding parameters as input arguments, and a number of model constants representing a given wheel, workpiece, and grinding fluid combination. The model constants are calibrated using measured grinding forces and power, taking into consideration of the chip formation, sliding, and plowing components of the power. These correspond to the constants of specific chip formation energy uch, the coefficient of friction μ and average contact pressure pa, and the plowing force per unit width F′pl, respectively. A new generalized experimental approach was developed in this study to reflect the physical meaning of the model. Compared with other approaches, this approach does not require a pre-knowledge of the wheel wear flat area value and the regime of the average contact pressure. The proposed approach includes measurements of forces and power from surface grinding tests with a fixed set of grinding parameters conducted at different wheel dullness levels for calibrating the constant μ, as well as tests with variable workspeeds at each of the wheel dullness level for calibrating the other constants. As an application example, it was applied to calibrating the power model for grinding of a nickel-based alloy with oil as grinding fluid and electroplated CBN wheels. The calibrated model was then validated through tests under different grinding conditions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".