Student for Master's thesis on the optimization of virtual AI sensors using MKS and FEM simulations of the chassis

Life is always about becoming... In life it is about going on a journey to become the best version of our future self. As we discover new things, we face challenges, master them and grow beyond us.

Apply to Mercedes-Benz and find the area where you can develop your talents individually. You will be supported by visionary colleagues who share your pioneering spirit. Joining us means becoming part of a global team whose goal is to build the most desirable cars in the world. Together for excellence.

Number: MER00042NP Classification: The RD/MDS department is responsible for the operational strength of the suspension and carries out, among other things, the corresponding proof of series release. From the development phase to the series release, we create load-collective calculations and measurements for the entire vehicle. In addition, we carry out strength calculations and tests, as well as screw fittings and experimental analyses for the chassis. The main basis for these tasks are load measurements in development vehicles and the knowledge of the expected loads in the customer vehicles used worldwide. The relevant load variables can usually not be detected directly by existing sensors. Therefore, expensive measuring technology is used in developing vehicles in order, for example, to measure component forces or drive torques. On the other hand, the relevant variables must be estimated at non-accessible places or for vehicles without special measuring technology. For this purpose, so-called virtual sensors are used which predict the loads based on existing signals. Topic and Problem: These virtual sensors are currently trained using classic error functions and validated on the basis of fictional standard assumptions of operational strength.

A comparison of these assumptions and metrics with the damage behavior based on FEM calculations at component level would significantly reduce the uncertainties in the evaluation and enable further optimization of the AI models.

Tasks and objectives: The aim of this work is to identify important frequency ranges, suitable fault functions and metrics as well as adapted operational strength parameters in order to use more effective and robust AI models in future vehicles.

For this purpose, extensive data from multi-body simulations and FEM models of the relevant components are available. On this data basis, you will develop creative methods for data augmentation (Rauschen, Frequency Filtering, etc.), perform simulations and work out their effect on component damage

Based on this, the training and evaluation methodology of virtual AI sensors is to be optimized

We recommend and offer a paid internship before the final thesis to ensure that there is sufficient time for placement and integration into the simulation environments. The aforementioned start and duration of this work are first proposals and can be flexibly adapted if necessary. The final topics will be discussed with the university, you and us.

Student*in Engineering, Computer Science, Mathematics or Comparable with reference to Simulation

Great enthusiasm and first experiences in and for signal processing, FEM and Data Science

Experience in Python/MATLAB programming for data analysis

Embossed and creative problem solving skills and self-sufficiency

Fun at scientific work

Bonus: knowledge of operational strength (Rainflow-Zählalgorithmus, Wöhlerlinie, etc.)

Bonus: knowledge of FEMSITE, Abaqus and multibody simulation

Additional information: We are looking forward to your online application with CV, lettering, certificates, current enrollment certificate, giving the semester and proof of the regular study period. Please do not forget to mark your documents as ''relevant for this application' in the online form and to observe the maximum file size of 5 MB. Further information on the setting criteria can be found here. Disabled and equalized applicants are welcome! The severely disabled representative (sbv-sindelfingen@mercedes-benz.com) is happy to support you in the application process. HR Services will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

Food supplements Employee handy possible Employee discounts possible Employee participation possible Staff Events Coaching Flexible working time possible Hybrid work possible Health measures Employment Mobility offers Kantine, Café Business doctor Child care Parking space Good connection Accessibility