Grouping and sequencing of machining operations for high-volume transfer lines
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
Transfer lines are employed for mass production of a fixed product or a very narrow range of product variants.This thesis considers a simple transfer line balancing problem with a focus on process planning and line configuration.Design features of the product are grouped and machining operations are sequenced in an optimal manner.The objective is to minimize the handling time fraction of the cycle time consisting mainly of orientation change time and tool change time.A new MILP model is developed to solve the problem with the aforementioned objectives while respecting a set of constraints, which include cutting tool allocation, tool magazine limit, tool life limit, takt time limit and precedence, inclusion & exclusion constraints.A problem-specific simulated annealing algorithm to solve large problems is also proposed.Numerical experiments are presented to illustrate the functionality of the MILP model and the meta-heuristic with respect to optimality and computation time.
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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 | 0.000 | 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