Parts Consolidation of Automotive Front Crossmember: From Two-Piece CFRP Design to One-Piece Design
Bibliographic record
Abstract
As demand for fuel efficiency rises, an increasing number of automotive companies are replacing their existing metal designs with carbon-fiber-reinforced polymer (CFRP) redesigns. Due to the handling and manufacturing processes associated with CFRP materials, engineers have more design freedom to create complex, light-weight designs, which would be infeasible to manufacture using metal. Additionally, it is likely that by redesigning with CFRP, many steel assemblies can be consolidated to significantly fewer parts, simplifying or potentially eliminating the assembly process. When designing an automotive crossmember using CFRP materials, designers often aim for a two-piece design (top and bottom), while utilizing reinforcement material where needed. The joining of these two pieces is typically accomplished with many mechanical fasteners and adhesives, significantly increasing the part count and the manufacturing complexity. The objective of this research project is to redesign an existing steel front crossmember to a one-piece CFRP design, removing the need for most of the mechanical fasteners and adhesives. While a reduction in stiffness was observed in the final design, strength requirements were met, and the overall mass was reduced by over 25%. This work shows that a one-piece CFRP crossmember is possible and should be further explored in the automotive industry.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".