Capacity scalability modeling and design framework for reconfigurable manufacturing systems.
Bibliographic record
Abstract
The focus of this research will be on how to approach the design reconfigurable manufacturing systems and how to control the design process. This will be achieved first in a systematic manner through implementing system design methodology to develop an architecture that visualizes the full reconfiguration process from recognizing customer needs through the operational level. An example in the reconfigurable printed circuit board (PCB) automatic assembly industry is used to illustrate the design and control activities in the proposed architecture. An analytical approach will follow the systematic approach. In this research only the first layer of the architecture dealing with capacity scalability is mathematically modeled. The capacity scalability model is used to develop a computer-based tool that generates optimal capacity scalability schedule and can be integrated to the architecture. Results of using the developed tool with numerical examples revealed the need to modify cost function of the model to reflect the real case of capacity scalability in reconfigurable manufacturing systems. The modification highlighted the fact that the success of reconfigurable manufacturing systems is through responsive scalable systems in a cost effective manner. Results also showed the superiority of the generated optimal capacity scalability schedule over other capacity plans and illustrated how the developed model can deal with different demand scenarios in an optimal way. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .D45. Source: Masters Abstracts International, Volume: 43-01, page: 0293. Adviser: Waguih El-Maraghy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.000 |
| 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) | 0.006 | 0.001 |
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".