PSO-Based Multidisciplinary Design Optimization of Automotive Assemblies
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Widely used in automotive industry, lightweight metallic structures are a key contributor to fuel efficiency and reduced emissions of vehicles. Lightweight structures are traditionally designed through employing the material distribution techniques sequentially. This approach often leads to non-optimal designs due to constricting the design space in each step of the design procedure. The current study presents a novel Multidisciplinary Design Optimization (MDO) framework developed to address this issue. Topology, topography, and gauge optimization techniques are employed in the development of design modules and Particle Swarm Optimization (PSO) algorithm is linked to the MDO framework to ensure efficient searching in large design spaces often encountered in automotive applications. The developed framework is then further tailored to the design of an automotive Cross-Car Beam (CCB) assembly. Sensitivity study is performed to identify the major contributor parts to the CCB mass and performance. The optimized CCB is shown to meet the design requirements while the mass and cost are considerably reduced.</div></div>
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".