Scheduling of the optimal tool replacement times in a flexible manufacturing system
Bibliographic record
Abstract
In Flexible Manufacturing Systems (FMSs). a cutting tool is frequently used for different operations and on different part types to minimize tool change-overs and the number of tools required, and to increase part-routing flexibility. In such situations, the tools become shared resources and work in job-dependent, changeable and nonhomogeneous conditions. It is well known that the tool failure rate depends on both age and machining conditions and that tool reliability is a function of the duration, machining conditions, and the sequence of the operations in FMS. The objective of this paper is to obtain a schedule of the optimal preventive replacement times for the cutting tools over a finite time horizon in a flexible manufacturing system. We assume that the tool will be replaced either upon failure during an operation or preventively after the completion of each operation, incurring different replacement costs. A standard stochastic dynamic programming approach is taken to obtain the optimal tool replacement times. The optimal schedule is obtained by minimizing the total expected cost over a finite time horizon for a given sequence of operations. A computational algorithm is developed and a numerical example is given to demonstrate the procedure.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".