Sheet Metal Forming Simulations with Elastic Dies: Emphasis on Computational Cost

Sammanfattning: The car industry produces many of their car parts by using sheet metal forming, where one of the most time-consuming phases is the development and manufacturing of new forming tools. As of today, when a new tool is to be evaluated in terms of usability, a forming simulation is conducted to predict possible failures before manufacturing. The assumption is then that the tools are rigid, and the only deformable part is the sheet metal itself. This is however not the case, since the tools also deform during the forming process. A previous research, which is the basis of this thesis, included a model with only elastic tools and showed results of high accuracy in comparison to using a rigid setup. However, this simulation is not optimal to implement for a daily based usage, since it requires high computational power and has a long simulation time.  The aim and scope for this thesis is to evaluate how a sheet metal forming simulation with elastic tool consideration can be reduced in terms of computational cost, by using the software LS-DYNA. A small deviation of the forming result is acceptable and the aim is to run the simulation with a 50-75 % reduction of time on fewer cores than the approximate 14 hours and 800 CPUs that the simulation requires today. The first step was to alter the geometry of the tools and evaluate the impact on the deformations of the blank. The elastic solid parts that only has small deformations are deleted and replaced by rigid surfaces, making the model partly elastic. Later, different decomposition methods are studied to determine what kind that makes the simulation run faster. At last, a scaling analysis is conducted to determine the range of computational power that is to be used to run the simulations as efficient as possible, and what part of the simulation that is affecting the simulation time the most. The correlation of major strain deviation between a fully elastic model and a partly elastic model showed results of high accuracy, as well as comparison with production measurements of a formed blank. The computational time is reduced by over 90 % when using approximately 65 % of the initial computational power. If the simulations are run with even less number of cores, 10 % of the initial number of CPUs, the simulation time is reduced by over 70 %. The conclusion of this work is that it is possible to run a partly elastic sheet metal forming simulation much more efficient than using a fully elastic model, without reliability problems of the forming results. This by reducing the number of elements, evaluate the decomposition method and by conducting a scaling analysis to evaluate the efficiency of computational power.