EFFECT OF FACE COOLING ON WORKPIECE TEMPERATURES USING 2D AND 3D FINITE ELEMENT ANALYSIS OF CREEP-FEED GRINDING
Bibliographic record
Abstract
This work uses validated 2D and 3D finite element models of the creep-feed grinding operation to study the effects of face cooling on the workpiece temperatures. The results show that as the intensity of the face cooling is increased the maximum contact temperature decreases and the location of the maximum contact temperature shifts away from the finished workpiece material and towards the uncut workpiece surface. The finite element models are also used to study the maximum temperatures along the workpiece during a complete grinding pass. The temperature profiles show that there are four important temperature features on the workpiece, which are the cut-in, steady-state, temperature spike, and cut-out zones. Cut-in occurs when the grinding wheel initially engages the workpiece, steady-state occurs in the middle of the workpiece, the temperature spike occurs at the beginning of cut-out, and cut-out occurs as the grinding wheel disengages from the workpiece. Finally, the results show that face cooling need only be applied to the area immediately adjacent to the contact zone to be effective and that there is very little benefit to applying coolant to the entire front and back workpiece faces.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".