Functional Synthesis of Manufacturing Systems Using Co-platforming
Bibliographic record
Abstract
Product changes propagate from design to manufacturing which requires dynamic scheduling and resources planning leading to costly physical changes in the manufacturing system. A mixed integer linear programming model is proposed in this paper for functional synthesis of manufacturing systems using co-platforming. Co-platforming is a methodology for synthesizing manufacturing systems through mapping product platform and non-platform features (and components) to platform and non-platform machines and capabilities, respectively. The objective function is to minimize the manufacturing system initial investment cost and the cost of changing it (addition or removal of machines) when the product family changes. A case study, based on data from automotive engine cylinder blocks manufacturer, is used to illustrate the effectiveness of the proposed model in synthesizing manufacturing systems as well as the cost savings achieved when applying Co-platforming. Co-platforming establishes strong mapping between products and systems platforms and enables synthesizing manufacturing systems which are capable of co-adaptation and co-evolution. Its application prolongs the life of the manufacturing system to be used for many product variants and generations while minimizing changes and related capital investments.
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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 | 0.000 |
| 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) | 0.003 | 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".