Optimization of Vehicle Steering Linkage With Respect to Handling Criteria Using Genetic Algorithm Methods
Bibliographic record
Abstract
The handling quality of a car is one of the most crucial parameters in the evaluation of the vehicle's overall performance. This quality is noticeably influenced by the structural and functional characteristics of the various components of the vehicle. The vehicle platform subsystems (i.e. steering, suspension, and braking) have major role in altering and tuning handling quality. It brings up special concerns in designing each of these mechanisms and need of having a comprehend understanding of their role in the handling characteristics of a vehicle. In this article, a general method for the optimization of steering system is presented. The investigation is focused on the geometrical parameters of a rack and pinion steering system, and their contribution on the handling characteristics. This kind of steering is common in medium class vehicles. A novel method is proposed to set the optimized geometry of the steering system, in particular its joint placements, by using a genetic-based approach. The cost function is composed of different criteria, which cover different aspects of handling characteristics. In order to eliminate the insignificant parameters, the sensitivity analysis is done using design of experiment method (DOE). A certified ADAMS model has been used as a benchmark to evaluate the presented model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".