Multiobjective robust design of the double wishbone suspension system based on particle swarm optimization

Area of Science:

• Mechanical Engineering
• Automotive Engineering
• Optimization Theory

Background:

• Vehicle suspension system performance is critical for overall vehicle design.
• Conventional optimization methods for double wishbone suspension lack robustness against design parameter deviations.
• Manufacturing uncertainties necessitate a robust design approach.

Purpose of the Study:

• To develop a robust optimization design for the double wishbone suspension system.
• To address uncertainties in manufacturing by considering design parameter deviations.
• To improve the reliability and performance of vehicle suspension systems.

Main Methods:

• A simplified double wishbone suspension model was created using ADAMS software.
• Design sensitivity analysis identified key design variables.
• Kriging models and Latin hypercube design were used for simulation experiments.
• A particle swarm optimization (PSO) method was applied to solve the robust optimization problem.

Main Results:

• Sensitivity analysis effectively determined critical design variables.
• Kriging models accurately fitted the mean and variance of performance characteristics.
• The PSO-based robust optimization successfully balanced performance mean and deviation.
• The developed method provides a trade-off between performance and its variability.

Conclusions:

• Robust optimization is essential for double wishbone suspension design considering manufacturing uncertainties.
• The proposed methodology effectively optimizes suspension performance under random variations.
• This approach enhances vehicle design by improving suspension system reliability and performance consistency.