Design Method of Under-body Platform Automotive Framing Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract OEMs across the globe are moving to reduce the number of their platform by 50% and to focus more on selected platforms to be used to design and produce different vehicles across segments and brands on a global scale (by size and price range) [1]. OEMs focused on platform sharing and standardization to rationalize their product development and production costs, besides significantly reducing the product-conception-to- launch time. As platform development costs, account for nearly half of the cost product development, this strategy of using common engineering across vehicle models allows money as well as time savings. This research introduces new systematic framework and methods dealing with a complete end-to-end design process of the production system for under-body platform complete in body and white (BIW). This method helps product developer and systems engineer to plan modular development (product upgrades) and interface for both manufacturing and final assembly of production systems. This research introduces new tools such Hybrid Design Structure Matrix (HDSM) to help system designers to understand changes in product design and mapping to physical domains (production systems) and to evaluate the effect of changes by connecting the process to digital manufacturing (DM) simulation. The methods will introduce a new approach to vehicle production systems design and development of “underbody platform” (BIW) using modular assembly strategy.
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.
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 | 0.001 | 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 | 0.001 |
| 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