Multi-Objective Optimization of Structural Design for Lightweight Vehicle Chassis

This study presents a systematic optimization of a lightweight vehicle chassis design using Design of Experiments (DoE), Finite Element Analysis (FEA), and Analysis of Variance (ANOVA) to enhance structural performance while balancing mass efficiency and safety factor. Material selection and wall thickness variations were considered to achieve a compromise between minimal mass and a safety factor of at least 1.5. Pareto front analysis, combined with the Taguchi method, identified the optimal solution, Cycle Design 11, which achieved a safety factor of 1.9489, representing an increase of 0.7681 compared to the baseline design. The total mass of 3.5742 kg reflects a 32.13% increase from the baseline. ANOVA results confirmed that both material and wall thickness significantly influence safety factor and mass, providing critical guidance for design decisions. This multi-objective optimization approach demonstrates that integrating FEA with experimental design enables superior chassis designs compared to traditional single-objective methods, offering a practical strategy for developing lightweight, safe, and energy-efficient vehicles.

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Publication Details

Journal
Automotive Experiences
Published
2025-09-25
DOI
https://doi.org/10.31603/ae.13567
Citations
1
Primary Topic
Economic and Technological Innovation
Type
article
Field-Weighted Citation Impact
3.13

Funders

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article

Multi-Objective Optimization of Structural Design for Lightweight Vehicle Chassis

1 citations
Automotive Experiences
Economic and Technological Innovation
3.13
article

Multi-Objective Optimization of Structural Design for Lightweight Vehicle Chassis

article en
1 citations

Abstract

This study presents a systematic optimization of a lightweight vehicle chassis design using Design of Experiments (DoE), Finite Element Analysis (FEA), and Analysis of Variance (ANOVA) to enhance structural performance while balancing mass efficiency and safety factor. Material selection and wall thickness variations were considered to achieve a compromise between minimal mass and a safety factor of at least 1.5. Pareto front analysis, combined with the Taguchi method, identified the optimal solution, Cycle Design 11, which achieved a safety factor of 1.9489, representing an increase of 0.7681 compared to the baseline design. The total mass of 3.5742 kg reflects a 32.13% increase from the baseline. ANOVA results confirmed that both material and wall thickness significantly influence safety factor and mass, providing critical guidance for design decisions. This multi-objective optimization approach demonstrates that integrating FEA with experimental design enables superior chassis designs compared to traditional single-objective methods, offering a practical strategy for developing lightweight, safe, and energy-efficient vehicles.

Automotive ExperiencesVol. 8(2)
State University of Malang (ID)
Universitas Negeri Malang
Affordable and clean energy
Openalex Percentile: Top 5%
Economic and Technological Innovation
3.13
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