Bibliographic record
Abstract
Grinding forces and power are important parameters for evaluating grinding process performance, and they are typically measured in grinding experiments. Forces are typically measured using a load cell or a dynamometer, whereas power is measured using an electrical power sensor to monitor the power of the spindle motor. Direct readings of the measurements include the net grinding force and power components for material removal and non-grinding components such as the impingement of a grinding fluid. Therefore, the net components must be extracted from the direct readings. An approach to extracting the net grinding forces and power is to perform additional spark-out grinding passes with no down feed. The forces and power recorded in a complete spark-out pass are used as the non-grinding components. Subsequently, the net grinding components are obtained by subtracting the non-grinding components from the corresponding totals for actual grinding passes. The approach becomes less accurate when large depths of cut, particularly large depths of cut and short grinding lengths, are involved. A new experimental approach is developed in this study to measure the non-grinding force and power components and to extract the net components. Compared with the existing approach, the new approach is more accurate for grinding with large depths of cut or short grinding lengths. In this approach, two additional grinding passes on an easy-to-grind material, one with and the other without a grinding fluid, are conducted using the same setup and condition as those in the actual test material to measure the forces and power for obtaining the non-grinding components. Subsequently, these non-grinding components are used as the non-grinding components of the actual material and subtracted from the total force and power components of the actual material to obtain the net values. To illustrate the application of the approach, surface grinding experiments are conducted to collect the forces and power. The extracted net power is consistent with the power predicted with the extracted net forces.
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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".