Bibliographic record
Abstract
<p>Grinding is on of the important machine processes when tight tolerances and fine surface finishes are required. However, due to the large number of process parameters involved, predicting the outcome of a grinding process is not a trivial task. This thesis describes the development of a predictive model of surface finish in the fine surface grinding process.</p> <p>The surface topography of a grinding wheel was analyzed using a laser scanner. The statistical distribution for grain protrusion heights and the transvere and longitudinal spacing of grains were determined. Each protruded grain is counted as a cutting edge that engages with the workpiece to generate a unique chip. A solid modeller was used to model an individiaul chip as an ellipsoid. The measured topography of the grinding wheel, together with a kinematic relationship in surface grinding, was used to determine the goemetrical characteristics of the ellipsoid. The solid modeller was then used to model the chip removal process by successive grains.</p> <p>The surface roughtness predicted by the model was compared with experiemental results. The results showed good consistency between the model and the actual surface properties.</p>
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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".