Methodology for plausibility checking of structural mechanics simulations using deep learning on existing simulation data

DS 91: Proceedings of NordDesign 2018, Linköping, Sweden, 14th - 17th August 2018

Year: 2018
Editor: Ekströmer, Philip; Schütte, Simon and Ölvander, Johan
Author: Spruegel, Tobias; Rothfelder, Richard; Bickel, Sebastian; Grauf, Andreas; Sauer, Christopher; Schleich, Benjamin; Wartzack, Sandro
Series: NordDESIGN
Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
ISBN: 978-91-7685-185-2


In modern product development, the use of sophisticated simulation tools for assessing the effects of design changes on the intended product behavior is essential. However, setting up valid simulations requires expert knowledge, acquired skills, and sufficient expertise. Design engineers, who perform finite element analysis (FEA) infrequently, must be assisted and their FEA results need to be checked for plausibility. An automatic plausibility check for finite element (FE) simulations in linear structural mechanics can identify non-plausible simulations and warn the user to interpret the results cautiously or ask for expert help. In this context, currently available tools can only compare very similar simulations. However, as the amount of available simulation data in the industry increases more and more, a data-driven simulation check is an obvious next step. Nevertheless, the question arises how simulation data of very different parts and simulations can be transferred to a single software tool, how this tool can learn the relevant rules behind plausible simulations, and how it can be applied to new simulations. In this context, it is especially important to train a metamodel that is able to generalize the rules so that it can later on be applied to unknown simulations. This paper presents an approach to transfer different FE meshes, corresponding FE results and boundary conditions to an individual matrix of fixed size for very different structural mechanic FE simulation. The novel approach uses spherical detector surfaces to project three-dimensional information on its surface. It allows generating the so-called “DNA of an FE simulation”; classification algorithms i.e. Support Vector Machines or Deep Learning Neural Networks such as Convolutional Neural Networks (CNN) can then classify this information. The whole methodology reduces the dimension of a 3D finite element simulation to a 2D matrix of numeric values. The matrix contains all the relevant information for the classification in “plausible” or “non-plausible”. An implausible simulation contains errors, which would be quickly identified by an experienced simulation engineer, whereas a plausible simulation does not contain such errors. As less experienced simulation users in design departments are not trained to find such errors in their simulation setup, they cannot detect them and take adequate countermeasures. In the paper, every single step of the novel methodology for plausibility checking of structural mechanics simulations will be illustrated and explained in detail for simplified parts and corresponding simulations.

Keywords: Plausibility Check, Convolutional Neural Network, Deep Learning, Data-driven approach, design automation, visualization


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