Determination of an Optimum Geometrical Arrangement of Workpiece in the Ultrasonic Elliptic-Vibration Shoe Centerless Grinding
Bibliographic record
Abstract
Abstract. This paper clarifies the influence of the geometrical arrangement of the workpiece on workpiece roundness in the ultrasonic elliptic-vibration shoe centerless grinding, and determines an optimum geometrical arrangement for minimizing the roundness error of the workpiece. The influence of the geometrical arrangements (, , ) of the workpiece on workpiece roundness were investigated by computer simulation involving a cylindrical workpiece of 5 mm in diameter with an initial roundness error of 25 µm. The results indicated that the final roundness error of the workpiece after grinding reaches a minimum at +=7 ° for various values of . It was found that the smaller the blade angle , the more precise the workpiece in terms of final roundness. Practical grinding operations involving pin shaped workpieces, such as SKH51, 5 mm in diameter and 15 mm in length, were carried out on the experimental apparatus previously developed. The experimental results agreed closely with those obtained by the simulation, showing that the optimum geometrical arrangement of the workpiece can be determined at +=7 ° and =60°, in which the workpiece roundness was improved from an initial roundness error of 25 µm to the final one of approximately 0.6 µm.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".