Abstract

For the robust and reliable design of products, the influence of tolerances must already be considered during the design process. Here it is important to have simulation methods available that not only map the function based on the nominal geometry, but also take the influence of tolerances into account. Statistical tolerancing results in a probability distribution of the resulting variants and thus also a probability of failure when each of the possible variants is simulated regarding function (e.g. strength, acoustics, etc.). However, this is where the challenge lies, as several thousand variants must be considered for statistical tolerancing. With conventional simulation methods (FEM, MBS, etc.), standard computers and average model sizes, the calculation of all tolerance situations would take several months. However, if the tolerances are not considered, there is a risk that the product will not be reliable under certain tolerance combinations. Unexpected failures can be the consequence. To include the statistical tolerance analysis in the functional analysis and thus ensure the reliability of the product, an alternative simulation method must be used. Machine learning methods can be the solution to create prediction models. These prediction models deliver results in real time and are therefore very well suited for carrying out a very large number of analyses in a short time. Using the example of a gearbox with two gears, the presentation shows how such a process can be realized with the 3DEXPERIENCE platform. In the example shown, the strength assessment for a statistical tolerance analysis with 10,000 variants can be reduced from 140 days to less than 4 days.

Slide deck

Presenter Bio

Jochen Kinzig is simulation consultant at CENIT AG. He studied mechanical engineering at the University of Karlsruhe. After graduation, he got into optimization and data analysis while working for the company FE-Design. In his following work at Schaeffler he built knowledge in EMC and Simulation Driven Design. At CENIT AG he is working in the service team for SIMULIA.