Part grouping and tool loading in versatile multi-tool machining centers
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
A central problem of tool management in Versatile Multi-tool machining centres is to decide how to batch the parts to be produced and what tools to allocate to the machine in order to maximize utilization of these expensive machines. Various authors have proposed heuristics and/or mathematical models to minimize the batches of parts to be manufactured in a production period. There is no comprehensive study reported to compare the number of actual batches (stoppages) formed with and without processing time considerations. In this paper, the sequential deterministic heuristics (SDHs) are appropriately adapted to include processing time of operations in the formation of groups. The modified heuristics are more realistic in reducing machine stoppages due to tools. Some stochastic search techniques have also been adapted to compute the number of groups. The results are compared with those obtained from SDHs and standard search techniques. The results indicate that the adapted search techniques are powerful approaches for forming optimum number of batches of parts and tools.
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.
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 it