Noise vibration and harshness (NVH) development often takes place in the later development phases. Shifting the optimization to the early digital development phase enables more parameters to participate in the optimization and leads to a more holistic development process. Digital NVH development often modifies system and component frequency response functions (FRFs) using finite element (FE) simulation. Currently, the often manual process of creating new FE models for modified designs makes a systematic evaluation of many designs difficult and time-consuming. In this paper, we take on these difficulties and use both a Direct Morphing approach and a Box Morphing approach to automatically adopt the first existing FE models to modified designs. We use the generated simulation results to fit metamodels describing the correlation between geometrical parameters and characteristic FRF values. These metamodels provide an easy and fast to use tool for designers to consider NVH demands. In a simulation example, we demonstrate the capabilities by modifying the kinematic hard points of a vehicle suspension and using them to modify the noise transfer sensitivity. We show that the metamodels can lead the digital design process to intuitively and specifically reduce characteristic component FRF values by changing the location of the component hard points.

Inside the passenger cabin of modern cars, lower noise levels from quieter engines make road noise more dominant. The main transfer path for road noise below 300 Hz into the car is the suspension. Suspension layouts are mainly determined by driving dynamics, but their influence on road noise is not in focus. Layout design changes for driving dynamics in the early development phase require the modification of structural dynamics Finite Element (FE) models used to predict interior acoustics. This manual modification makes acoustical effects from layout design changes difficult to predict. In the following article, we present a method to adapt suspension FE models automatically to suspension layout changes. This allows an automatic optimization of the suspension layout regarding road noise. As an example, a rear axle suspension layout is modified to decrease road noise between 60 and 90 Hz by moving the connection point between the track rod and the knuckle.

In modern vehicle development, suspension components have to meet many boundary conditions. In noise, vibration, and harshness (NVH) development these are for example eigenfrequencies and frequency response function (FRF) amplitudes. Component geometry parameters, for example kinematic hard points, often affect multiple of these targets in a non intuitive way. In this article, we present a practical approach to find optimized parameters for a component design, which fulfills an FRF target curve. By morphing an initial component finite element model we create training data for an artificial neural network (ANN) which predicts FRFs from geometry parameter input. Then the ANN serves as a metamodel for an evolutionary algorithm optimizer which identifies fitting geometry parameter sets, meeting an FRF target curve. The methodology enables a component design which considers an FRF as a component target. In multiple simulation examples we demonstrate the capability of identifying component designs modifying specific eigenfrequency or amplitude features of the FRFs.