In the early stage of vehicle development process, it is customary to establish a set of goals for each kinematic and compliance (K&C) characteristic and try to find out design variables such as the location of hard points and bushing stiffness which can achieve these goals.
However, since it is very difficult to find out adequate set of design variables which satisfy all the goals, many engineers should rely on their own experiences and intuitions, or repeat trial and error to design a new suspension and improve old one.
In this research, we develop a suspension design process by which suspension K&C characteristic targets can be achieved systemically and automatically. For this purpose, design optimization schemes such as design of experiments (DoE) and gradient-based local optimization algorithm are adopted.
Firstly, sensitivity analysis is carried out using DoE and then, according to the analysis results, the whole problem is divided into two partial problems which are independent of each other. And then, these two independent multi-objective optimization problems are solved using gradient-based optimization algorithm.
With this two-stage process, K&C characteristics of McPherson strut type front suspension are optimized successfully. Since the size of a partial problem is quite small compared to original one, we can obtain computational efficiency and fast convergence as well.