A corrective assembly method using a buffer in a high-precision machining-assembly production system
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".