
The digital twin – driving innovation development the smart way
During the Co-Creation of our new eDrive we’ve consistently focused on the development of a digital twin, which has enabled us to carry out simulations directly in a real-world scenario and identify weak points during the development phase - without having to create expensive prototypes.
Using the digital twin, we simulated system interactions of motor and housing in advance, as well as testing various different materials such as laminated steel and SMC.
What tolerances can we allow?
The digital twin shows it!
Goal: To simulate the acoustic effect on component X through the change in gap dimension as realistically as possible using our digital twin – because high tolerance requirements are costly. We've used the digital twin to find out which tolerances we are willing and able to permit in the physical implementation.
Fact: Generally, simulations represent the ideal case and tolerances are either ignored or set too tight.
Consequence: The physical result deviates approx. 20 - 30% from the simulation.

How did we proceed?
Step 1: Design of the simulation model
We worked with existing digital images, which were then additionally linked to real sensor data, a database and analysis software.
Manufacturing of stator and rotor
Step 2: Tolerance measurement on the real prototype


Consideration of the tolerances in the simulation
Step 3: Digital Twin
As part of our DoE (Design of Experiments) we simulated playing around with different tolerances in order to identify effects on the overall project.
In this case, the real tolerance of 1/10 mm (deviation of teeth) was included in the digital simulation.
The result: maximum proximity to the original – significant savings in time and costs through our rigorous DoE approach.

Testsimulation
The result: Maximum proximity to the original
Conclusion: Significantly shifted frequencies that lead to additional vibration excitation and thus to efficiency losses - or NVH influences.


Advantages of using a digital twin:
- Innovations are moved forward quickly
- Reduction of development costs & Design to Cost
- Recognition of cross-system correlations
- Intelligent optimization based on collected data
- Identification of critical components and interfaces
- Efficient co-creation and information exchange via the digital twin