Improvement of track zero to increase read/write area in hard disk drive assembly process
Bibliographic record
Abstract
Article history: Received January 10, 2013 Received in revised format 19 July 2013 Accepted July 19 2013 Available online July 19 2013 An improper machine setting in a hard disk drive assembly process could reduce the read/write area of hard disk drives. This paper presents the methodology to increase the read/write area of hard disk drives by finding an optimal machine setting that minimizes the track zero. The Six Sigma improvement approach was applied. The design of experiment technique helped indicate the optimal levels of significant factors, which were the number of screw turn, the rotating pin height, and the cylinder force, that yield the minimum track zero. The results showed that the mean of track zero was decreased from 16,185 to 15,120 tracks and the standard deviation was decreased from 1,116 to 633 tracks resulting in the increase of the process capability index (Ppk) of the track zero performance from 0.54 to 1.52. © 2013 Growing Science Ltd. All rights reserved.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".