A Methodology for Modular and Changeable Design Architecture and Application in Automotive Framing Systems
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".