Further Training Seminars in Vehicle Development

Bild eines Mercedes-Benz Actros - Bei der Auslegung und Beurteilung mechanisch beanspruchter Bauteile hinsichtlich ihrer Betriebsfestigkeit/Zuverlässigkeit spielen statistische Methoden eine zentrale Rolle.
© Daimler AG/Fraunhofer ITWM

Practical applied research and method development requires a constant exchange with the world of industrial applications. This involves the transfer of know-how from the institute to industry, the communication of new processes and methods within the application context as well as the necessary calibration of the new methods upon their real benefit.

Of course, this also happens within the scope of our projects. In addition, however, we have decided to create an even broader and more open instrument beyond seminars for engineers in industry. We work on the interdisciplinary topics in which we are active in research and application, communicate the relevant basics and explain the current technical limits.

Symbolbild für den Themenbereich Statistische Methoden am Fraunhofer ITWM
© John Deere
Statistical methods play a central role in the design and evaluation of mechanically stressed components with regard to their fatigue strength/reliability.


The Fraunhofer Institute for Industrial Mathematics ITWM offers the following seminars periodically:

  • Further training in the field of »Load data analysis, design and simulation«:
    • Basics of load data analysis and operational stability
    • Data reduction in the time and amplitude range
    • Method of analysis in the frequency domain
    • What loads must be designed for?
    • Design bases and load data synthesis
    • Basics of multi-body simulation (MBS)
    • Shortening and simplification of field trials
    • Use of vehicle-independent data – VMC
  • Further / advanced training in the field of »Statistical Methods in Industrial Strength«.
    • How to model different types of use and customer demands?
    • Which influencing variables are important, which may be redundant?
    • How can they be translated into test tracks or test programs?
    • Which failure (un)probabilities do you have to prove and how?


Further information