Ford Motor Company Uses Digimat for NVH Optimization

Ford Motor Company Uses Digimat for NVH Optimization

Advanced anisotropic damping modeling for NVH optimization

The Ford Motor Company uses glass fiber reinforced plastic materials for powertrain parts such as engine covers, air intakes and engine oil pans. These materials offer numerous benefits with regards to automotive component design including lightweighting and a better damping compared to metal. However, due to the complexities of the composite material microstructure which arise due to the manufacturing process, both the stiffness and damping of reinforced plastic materials are frequency dependent and locally anisotropic. The efficient design of such components from an NVH perspective requires the use of adapted techniques that can account for these specificities, from the material characterization stage up to the performance prediction of each design iteration.

The challenge can be framed as follows:

  • How to build a material model capable of capturing the correct local anisotropic stiffness and damping behaviors depending on the frequency and the local fiber orientation?
  • How do the microstructure parameters influence the part’s NVH behavior?
  • The Ford Motor Company can see a great opportunity to employ this technique not only as a predictive simulation methodology but also as a tool to fine-tune the parameters which drive a given part’s microstructure. This brings 2 benefits:
  • The part’s NVH performance as well as weight will be optimized
  • It reduces the need for corrective actions which typically increase component weight in order to meet acoustic targets


The technology in Digimat has been applied on 2 components. An engine oil pan in order to:

  • Apply the procedure to create a visco-elastic material model from DMA test data
  • Demonstrate the accurate prediction of eigenfrequencies and acceleration peaks when applied on a FRF FEA compared to experiment.

An engine bracket in order to:

  • Estimate the extent to which fiber orientation, fiber mass fraction and fiber length can influence a component’s NVH behavior

For each application, the fiber orientation distributions have been mapped onto the Nastran structural meshes. The effect of the manufacturing process on the prediction of the components behavior is taken into account via the mapped fiber orientations which have been associated with a multi-scale visco-elastic material model in FEA. For the engine bracket application, the mass fraction and the fiber length have been modified directly in the material file in order to gauge their influence on the simulation.

“I’m very impressed with the unique adaptive capabilities demonstrated by Digimat as part of our partnership methodology research project with e-Xstream. Use of this software will allow us to optimize the microstructure of design of composite materials in such a way that we can tune for specific NVH requirements. Automotive application of composites is essential for weight reduction, resulting in less fuel consumption and reduction of CO2 emissions. However, at times this may come with some level of degradation in NVH performance. Digimat will allow the engineer to properly model & design the material composition such the optimum damping tuning can be achieved to deliver the required NVH performance and refinement. This is a huge step forward in light weighting component design!”

– Mario Felice, Manager , Global Powertrain NVH CAE, Ford Motor Company


The engine oil pan case study has demonstrated a significant improvement in how frequencies and acceleration peaks are identified compared to the usual isotropic method applied by Ford Motor Company: see Fig. 1

The engine bracket case study reveals that each microstructure parameter (the fiber orientation, the fiber length and the fiber mass fraction) has a significant potential influence on the component’s performance, enabling them to be considered as design parameters. Hence, by fine-tuning the material microstructure of the injected composite, a design engineer can optimize the component’s performance in terms of damping and lightweighting:

NVH optimization

Read Case Study


  • shashikant Sharma
    January 22, 2021


    We’re interested in learning more about this study. Can someone plz contact me?


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