A corrective assembly method using a buffer in a high-precision machining-assembly production system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electric relay manufacture and assembly is an example of high-precision machining followed by an assembly process. During machining, parts exhibit dimensional variance and manufacturers have several techniques and strategies for how to maximise production and improve efficiency when variance is present. One approach is to measure the variance, select parts appropriately for the best match, perform the minimum amount of adjustment and rework, and then perform the assembly. This approach is difficult in practice because measurement errors also occur and confound the knowledge about each part's dimensions. In this paper, we consider a new matching approach to increase the production rate. We propose a part-combination selection method in which a pair of assembly parts in buffers is optimally selected using a target of an estimated assembly error, and an adjustment machine is then optimally selected using a range of estimated assembly errors. Furthermore, we consider an analytical approach to estimate the optimal control parameters for the proposed method that yield the maximum production rate. The analysis results show that the analytical approach can estimate the nearly optimal control parameters, including the nearly maximum production rate in any buffer capacity. The results also show that by installing a small buffer capacity, the production rate can be increased.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it