Precision Machining of Mechanical Seals on the Machine "Rastr 220"
Bibliographic record
Abstract
Here we present the "raster" method of final abrasive treatment of surfaces. Here we describe the theoretical foundations and patterns of "raster" processing method. We also describe the kinematics of lapping and polishing machine "Rastr 220". For pieces of brittle composite material based on graphite, we have designed and manufactured a universal multipiece arrangement. The number of simultaneously machined pieces and their location in the device shall provide an even lapping wear. For this purpose, the axis of rotation of the tool shall be offset about its axis of symmetry to 5-15 mm. The device of larger diameter at the lapping should provide for periodic lapping bleed of the pieces by a certain amount. However, from the viewpoint of surface grit, a high lapping bleed of the pieces is not desired. In the process of lapping, the device shall be fully loaded with pieces. In case of impossibility to meet these requirements, it is necessary to periodically change the position of pieces in it. The device allows to compensate for different height of processable workpieces, to provide for their self-aligning on the tool surface, and to place the pieces evenly over the surface of lapping. The experimental testing of the treatment process on the machines with "raster" kinematics of the tool working movement was carried out. The comprehensive studies of precision treatment process for high-precision pieces made of composite materials based on siliconized graphite of grade (GAKK 55/40) were conducted. The requirements for working planes of the pieces are the following- Ra not more than 0.1 µm, flatness deviation not more than 0.6 µm. An estimation method for surface indulation was offered, and common technological advice on the implementation of "raster" lapping process for siliconized graphite pieces on the machine "Rastr 220" was developed.
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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 | 0.001 | 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 | 0.001 | 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".