In automotive industry, virtual tools and methods are becoming increasingly important to ensure robust solutions as early as possible in the development processes. Today, techniques exist that combine Monte Carlo simulations (MCS) with finite element analysis (FEA) to capture the part's nonrigid geometric behavior when predicting variation in a critical dimension of a subassembly or product. A direct combination of MCS with full FEA requires high computational power and the calculations tend to be very time consuming. To overcome this problem, the method of influence coefficients (MIC) was proposed by Liu and Hu in the late 1990s. This well-known technique has since then been used in several studies of nonrigid assemblies and sensitivity analysis of the geometric fault propagation in multistation assembly processes. In detailed studies of the resulting subassemblies and levels of variation, functionality for color plots and the ability to study the geometry in arbitrary sections are desired to facilitate the analysis of the simulation results. However, when including all part nodes in combination with methods for contact and spot weld sequence modeling, the required sensitivity matrices grow exponentially. In this paper, a method is proposed, describing how traditional MIC calculations can be combined with a separate detailed subassembly analysis model, keeping the model sizes down and thus facilitating detailed studies of larger assembly structures.
Method for Handling Model Growth in Nonrigid Variation Simulation of Sheet Metal Assemblies
Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 4, 2014; final manuscript received February 18, 2014; published online April 28, 2014. Editor: Bahram Ravani.
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Lindau, B., Wärmefjord, K., Lindkvist, L., and Söderberg, R. (April 28, 2014). "Method for Handling Model Growth in Nonrigid Variation Simulation of Sheet Metal Assemblies." ASME. J. Comput. Inf. Sci. Eng. September 2014; 14(3): 031004. https://doi.org/10.1115/1.4027149
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