Topology, size and shape optimization of an automotive cross car beam
Bibliographic record
Abstract
An automotive cross car beam supports instrument panels including the heating, ventilation and air-conditioning system, the knee airbags, the steering-column and steering-wheel system and the central console. Avoiding resonant frequencies and improving driving comfort are major performance requirements in the design of a cross car beam. Because of the nature of mass production in the automotive industry, the consideration of manufacturability is important, and the current practice in the industry does grant detailed information on the steering-column and steering-wheel system to the cross car beam designer. The objective of this paper is to perform a complete topology, size and shape optimization of a cross car beam by using a lightweight material, by considering two practical manufacturing processes (extrusion and casting) and by assuming a realistic situation where only limited information on the steering-column and steering-wheel system is available to the cross car beam designer. First, a simplified finite element model of the steering-column and steering-wheel system was developed, and it was calibrated using optimization such that the important behaviour of the simplified finite element model agrees with that of the real steering-column and steering-wheel system. Topology optimization was performed to determine the optimal material distribution for the parts that connect the steering-column and steering-wheel system and the cross car beam. Then a geometry reinterpretation of the favourable topology result was performed to address the concerns from the viewpoints of the cost and the manufacturability. A sensitivity study was conducted subsequently to determine the size optimization design variables with significant effects on the frequency performance. Finally, size and shape optimization were performed together to optimize further the details of the cross car beam structure. The weight of the optimal aluminium design was reduced by nearly 40% compared with the steel design while the important performance requirements are met.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".