## 5 Conclusions  
In this paper, we used two different morphing approaches to automatically batch morph FE models in the early digital development phase. The received FRF data was used to create metamodels describing the coherence between component hard point coordinates and characteristic FRF values. These metamodels were used to create a new component geometry that optimizes, for example, the transferred force at a specific frequency. This enables us to shift NVH optimization to earlier design phases and automate the creation of design recommendations to modify FRFs. The validation proved both morphing approaches suitable to create metamodels, leading the design process in the right direction. As the creation of control blocks is quite time-consuming, the Box Morphing approach is suited for simpler geometries or complex geometries that require detailed feature preservation. For faster investigations, the Direct Morphing approach proved to be working well and easy adoptable to new components. For both approaches, the geometry changes on a complex component are restricted to small displacements, which makes larger topology investigations impossible. This is why the presented approaches especially apply to the early digital development phase, when the rough topology design is already concluded, but the specific component design is yet to be completed. The metamodels proved to be able to direct the design process in the right direction. After creating the morphing environments, the automatically performed FE calculation, necessary to generate the metamodels, is the most time-consuming process. Both morphing approaches hinted the design to a reduction of a specific FRF peak. The presented simpler metamodels like two dimensional polynomials are suitable for quick design choices without having to perform numerical optimizations. This can help the component designer to consider NVH demands more easily without the need to create new FE models or even produce hardware prototypes. This can prevent dead-end designs to be implemented much earlier. The more complex higher dimensional metamodels though enable holistic optimizations to conclude optimum geometry designs. Additionally, the metamodel is able to perform a robustness analysis to determine the influences of geometric component tolerances. The presented study showed that simple metamodels are able to direct the design process in the early digital design phase without the need for numerical optimization. In this phase, while many parameters are still to be discussed, a hint for the direction of change could be more valuable than a specific optimized design. Nevertheless, the already simulated data enables an optimization without any additional simulation effort. The used metamodels can still receive improvement for more complex problems. For example, instead of polynomials, we could use moving least squares or Kriging approaches, or even machine learning algorithms like neural networks. The NVH optimization presented in this paper is the first step in a holistic optimization of the complete system, in our case the full vehicle. We can deploy the modified component into the full transfer path from road to passenger ear. The optimization of the sound pressure level is possible in a similar manner, as presented here. Kinematic hard points can serve as input for metamodels that predict the sound pressure level in the passenger cabin. The component and system designers obtain an easy to use tool to consider NVH demands in the early, digital design phase. Conceptualization, T.v.W., M.L. and F.G.; methodology, T.v.W. and M.L. ; software, T.v.W. and M.L. ; validation, T.v.W. and M.L. ; writing--original draft preparation, T.v.W. and M.L. ; writing--review and editing, T.v.W., M.L. and F.G.; visualization, T.v.W. ; supervision, F.G.; project administration, T.v.W. All authors have read and agreed to the published version of the manuscript. This research received funding by Mercedes-Benz AG.