Tolerance Simulation of Compliant Sheet Metal Assemblies Using Automatic Node-Based Contact Detection
Paper in proceedings, 2008
Tolerance simulation is a crucial tool for predicting the outcome in critical dimensions, used during early phases of product development in automotive industry. In order to increase the accuracy and the agreement with reality of the predictions even further, variation simulation software offer in some cases the possibility to perform compliant analysis, i.e. the parts are not restricted to be rigid. In the compliant analysis, contact modeling is an important tool to avoid that parts penetrate each other in the simulations. In this paper a simplified method for automatic contact detection, well suited for tolerance simulations, is suggested. The method is based on node to node contacts instead of contacts between a node and a surface, which is a common procedure. Using automatic contact detection can in many cases give rise to an excessively large number of contact pairs. Therefore, an algorithm for attenuation of the contact pairs is also presented.
Traditionally, non-rigid variation simulations with contact modeling are very time consuming, but by using this kind of simplified contact modeling in combination with the Method of Influence Coefficient in the Monte Carlo simulations, the simulation times can be kept down. The method is tested on an industrial case study, with respect to both standard deviation and position. The correlation between simulated data and industrial inspection data is high and there is a considerable difference between simulations with and without contact modeling, showing that this is an important feature in non-rigid simulations. Inspection data is also compared to rigid simulations and to simulations with different number of contact pairs.
The computational effort for the suggested node-node based contact modeling algorithm seems to be considerable less than when using traditional finite element software, but still, the agreement with inspection data is very good.