Modelling and FE Simulation of HVC Using Multi Objective Response Surface Optimization Techniques
Bibliographic record
Abstract
The purpose of an automotive chassis is to maintain the shape of the vehicle and bear the various loads that are applied to the vehicle. The structure typically accounts for a large portion of the development and production costs of the new vehicle program, and the designer has many different structural concepts available. Choosing the best is important to ensure acceptable structural performance under other design constraints, such as cost, volume and method of production, product application, and more. The material selection for chassis depends upon various factors like lightness, economy, safety, recyclability, and circulation of life. The current study aims to perform optimization of the design of a heavy vehicle chassis using central composite design & optimal space fill design scheme (s) with the material tested is Al6092/SiC/17.5p MMC. Different design points are generated using design of experiments. The equivalent stress, deformation and mass are evaluated for each design points. The CAD modelling and FE simulation of heavy motor vehicle chassis is conducted using ANSYS software. From the optimization conducted on chassis design, response surface plots of equivalent stress, deformation and mass are generated which enabled to determine the range of dimensions for which these parameters are maximum or minimum. The use of Discontinuously Reinforced Aluminium-Matrix Composites Al6092/SiC/17.5p MMC aided to reduce weight of chassis by 66.25% and 66.68% by using CCD and Optimal space fill design scheme respectively, without much reduction in strength of chassis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".