Deep Profiled Slot Grinding on a Nickel-Based Alloy with Electroplated CBN Wheels
Bibliographic record
Abstract
A process for grinding deep profiled slots in a nickel-based alloy with electroplated cubic boron nitride (CBN) wheels and straight oil is presented. These slots were prepared by this process for further grinding with electroplated CBN quills to generate the final fir-tree slots in gas turbine disks. Fir-tree slots are usually machined using broaching. The application of broaching, however, is limited in the case of nickel-based powder metal alloys due to short life of broaching tools and the effect on machined surface integrity. Grinding tests were first conducted on rectangular blocks to grind slots without inclinations at a fixed wheel speed vs = 60 m/s to identify the combinations of depths of cut, workspeed, and up/down grinding satisfying the requirements of ground surface quality and material removal rate. Inclined slots were then ground with the identified condition on a block representing a segment of an actual turbine disk to validate the condition. The wheel life was finally tested by grinding all the slots on the actual disk. Grinding power was measured, and the ground surfaces were inspected for any sign of burning. Preset target material removal rate and wheel life were obtained. It was found that electroplated CBN wheels are capable of grinding deep profiled slots on the difficult-to-cut nickel-based alloy.
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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 | 0.000 | 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) | 0.001 | 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".