Optimal Design of a Reconfigurable Machine Tool Considering Machine Configurations and Configuration Changes
Bibliographic record
Abstract
A reconfigurable machine tool (RMT) is used as a group of machines by changing its configurations for different machining functions such as milling and turning. An optimization approach is introduced in this research for the design of a RMT based on evaluations to both the different machine configurations and the reconfiguration processes to change between machine configurations. In this research, different design candidates, machine configurations for each design candidate, and parameters of the machine configurations are modeled by a generic design AND-OR tree based on design requirements. A specific design solution modeled by multiple machine configurations and their parameters is created from the generic design AND-OR tree by tree-based search. For each design solution, reconfiguration process to change from one machine configuration to another configuration is modeled by a generic process AND-OR graph that is composed of operation candidates, sequential constraints among operations and operation parameters. A specific process solution is created from the generic process AND-OR graph by graph-based search. A multi-level and multi-objective optimization method is developed to obtain the optimal design that is modeled by its machine configurations, parameters of machine configurations, reconfiguration processes to change between machine configurations, and parameters of reconfiguration processes. A case study is implemented to demonstrate the effectiveness of this new optimal RMT design approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".