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Development of Simple Connection Model for Plastic Parts in Low-Speed Crash Simulation

Collision performance is evaluated by CAE not only for metal parts such as steel and aluminum but also for plastic parts. As it takes time to create molds for a large part such as the bumper face, ensuring the performance by pre-calculation is important to shorten the development period. When mold production is started prior to the test to reduce the development time and if failures occur in the test, it will take time and cost to modify the mold. Therefore, high calculation accuracy is desired, but the test reproducibility is not satisfactory in some areas. Out of those areas undergoing enhancement efforts, this document introduces our efforts for plastic parts connection coming-off.

Modeling of Bolts using the GISSMO Model for Crash Analysis

The prediction accuracy of bolted connections is becoming increasingly important in the automotive sector. The requirements and thus the vehicle architectures are changing due to the electrification of vehicles and the high weight of batteries as well as their low permissible intrusion depth. Bolts are required as detachable fasteners to connect batteries with the body in white. The energy absorption concepts of vehicles with internal combustion engines have been continuously developed over the past decades. Thanks to many years of experience, the bolt connection behavior and load transfer are well known. Energy absorption concepts for electrically powered vehicles are in a comparatively early development phase. For the evaluation and further development of new crash concepts, a reliable simulation method is a basic requirement to predict joint failure in bolted connections.

Estimation of Spot Weld Design Parameters using Deep Learning

In automotive production, each automobile has approximately 7,000 to 12,000 spot welds along with other kinds of connections. The position of the spot weld with respect to the flange and the distance between the spot welds as well as various other parameters usually vary for each part combination (spot weld design). If these properties are known, they can be used for automatic generation of spot welds during the design phase of the product development which is otherwise a cumbersome manual process. The spot weld design to be determined by the engineer depends on many factors (input parameters) such as loads and forces that might be applied to the structure, material combination, geometry of the parts, connection technology and its process parameters. Some of these parameters such as material combination and geometry of the parts are predefined by the designer or are results of the circumstances such as loads and forces applied at the connection. The remaining parameters such as connection technology, process parameters, spot weld distances and flange distance have to be chosen by the engineer. On the basis of existing designs and with help of machine learning techniques it may be possible to predict the spot weld design parameters like spot weld distance and flange distance. Within this work existing spot-weld designs are extracted from a vast amount of FEM simulation input data available in the Simulation Data Management (SDM) system LoCo of SCALE GmbH and applied as the basis for training and benchmarking new methods for estimating spot weld parameters.