Investigation of the fake reject cases with unqualified operational dimension based on the error compensation and tolerance compression
Bibliographic record
Abstract
A fake reject (FR) with unqualified operational dimension often occurs during part machining when the operational data do not coincide with the design data, which can lead to unnecessary waste. We investigated novel judgment and remedial measures for FR to enhance the product qualification rate. We first discuss the reasons for FRs that occur when the calculation for the operational tolerance of the dimension chain is too narrow when calculated using the worst-case scenario method for the process. Following that, we present a novel method for estimating FRs by calculating a new dimension chain. The operational dimension is treated as the concluding link. The actual deviations of the dimensions generated before the operational dimension are used to replace their upper and lower deviations. Finally, based on the error compensation relationship among the component links in the process dimension chain, we propose a novel remedial measure for FR via compressing the dimension tolerance in subsequent processing. We also calculated the dimension tolerance after tolerance compression. This results from this study contribute to the assessment and processing of FRs in part machining.
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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 | 0.000 | 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) | 0.000 | 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".