A Modeling Approach for Elastic Tolerance Simulation of the Body in White Hang-On Parts

Abstract

Computer aided tolerancing (CAT) in the automobile industry is implemented by CAD tools. These tools analyze the manufacturability of complex assemblies with rigid single parts in an early stage to reduce the product development time and the cost for hardware prototypes. This paper proposes an approach to implement tolerance simulation for a compliant assembly, which includes manufacturing processes such as clinching, bolting and hemming by applying tolerance simulation tool. The fender- BIW system is simulated as a compliant–rigid system and the simulation model is applied to two production scenarios. The simulation results are compared with real measurement data, which demonstrates the efficacy of using simulation in early production as opposed to prototyping or other methods of design by showing the strong correlation between simulation results and as-built products.

Solving basic problems of compliant tolerance analysis by static analogy

“…More complex models of geometric covariance are obtained by combining the MIC with various statistical shape models [16][17][18]. By neglecting less relevant patterns, the MIC-covariance methods further reduce the number of needed FEM runs; their accuracy has been verified experimentally with good results in [19]. Other studies have modified the MIC to avoid that the calculated deformation leads to penetration between the parts; different algorithms for contact modeling are proposed in [20][21][22][23].…”

Challenges Faced While Designing Body in White

“…However, the conventional statistical tolerance fails to justify the actual manufacturing process. [8] says the rigid tolerance simulation does not include any kind of deformation of parts that occurs due to clamping, welding, clinching, riveting or other operations. Finite Element Modeling simulation can outweigh this flaw by simulating the compliant part; with condition that model must be modified in each case and also the length of simulation run time scales with model size and complexity.…”