EFFECT OF WHEEL WEAR ON CONTACT LENGTH, UNCUT CHIP THICKNESS AND FORCES FOR A DEFORMABLE GRINDING WHEEL AND WORKPIECE
Bibliographic record
Abstract
In this paper, contact length, uncut chip thickness, and contact forces were studied as the wheel wore during dry surface grinding of 4130 normalized steel for three different depths of cut (0.0025, 0.005, 0.0075 mm). An aluminum oxide grinding wheel (Norton 38A46HVBE) was used for all the experiments and the work and wheel speeds were 0.22 and 32.3 m/s, respectively. In each experiment, the wear flat area, the cutting edge density, the cutting edge width and length were determined using an automated optical measurement system. The grinding forces were measured using a force dynamometer. The contact length was determined using rigid-body, smooth-body and rough-body contact assumptions. The uncut chip thickness was determined using a continuity analysis. In this approach, the average volume of material is first determined by dividing the total material removal rate by the number of cutting edges. Then an assumption of the shape of the chip is made. In this work, the uncut chip was assumed to have a triangular profile and a rectangular cross-section. The uncut chip thickness can then be determined by dividing the average chip volume by the average contact length and chip width. In the experiments, grinding forces, wear flat area, cutting edge density increased and uncut chip thickness decreased as a result of wheel wear. In addition, the wheel wear increased the contact length significantly in the rough-body assumption, marginally in the smooth-body. assumption but not in the rigid-body assumption. The normal contact pressure was determined by dividing the normal force by the product of the percent wear flat area and contact area. This result suggested that the rough body assumption represented the data most accurately.
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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".