A Methodology for Modular and Changeable Design Architecture and Application in Automotive Framing Systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a design methodology to modularize integrated fixtures, such as automotive framing systems, to be quickly and cost effectively reconfigured to accommodate a variety of products. Automotive assembly framing systems are used to accurately position and spot-weld the loosely pre-assembled body-in-white (BIW) car body parts. Auto-assembly systems can handle many car body styles; however, the used model-specific BIW framing systems are large, expensive, and the changeover to accommodate different car models takes considerable time. The proposed modularization design methodology aggregates a set of design structure matrices (DSMs) to represent the required changes in the fixtures, the spatial relationships between the used tools and fixtures, and the flow of exchanged information between them. The best granularity level of the modular fixture design architecture is determined using “Cladistics”: a hierarchical biological classification tool. Different tools within the framing system are combined into switchable modules, which allows these integrated systems to be easily reconfigured between different car body styles (product variants). A case study involving four car body styles is used for illustrating the presented design methodology. Results show the validity of the proposed methodology and demonstrate the obtained design of new modular automotive BIW framing system and the methods used for postprocessing and redesigning to improve the framing system's changeability.
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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 | 0.002 | 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