<p>Geometric variation causes functional and aesthetic problems in the assemblies. The challenge is predicting the moments of the distribution of geometric variation of assemblies to evaluate compliance with the set requirements. Variation analysis of non-rigid sheet metal assemblies with respect to the geometrical quality is traditionally performed using Method of Influence Coefficient (MIC), based on Finite Element Method (FEM) and the Monte Carlo Simulations (MCS). The joining operation is one of the most crucial steps in the assembly process, imposing forces on the parts and causes bending and deformation during the assembly, consequently contributing considerably to the final geometrical outcome of the assembly. To model the behavior of the assembly realistically and to achieve accurate simulation results considering the sequence of joining is essential. In a digital twin of the assembly process, where the scanned geometry of parts are available and the assembly parameters are optimized for a batch of fabricated components, a joining sequence needs to be provided for the optimal geometrical outcome of the batch of assemblies. The sequence optimization of the joining processes is a time-consuming combinatorial problem to solve. Variation analysis of non-rigid assemblies with stochastic part inputs and including joining sequences requires an extensive amount of computational effort. More efficient approaches for evaluating assembly geometric variation are desired. In this paper, a computationally efficient approach is proposed for geometric variation simulation and optimization of non-rigid assemblies with stochastic part inputs with respect to the joining sequences. A clustering approach is proposed categorizing the incoming parts based on the part variation. A decomposition approach is considered for identifying the assembly behavior during joining. For each of the clusters of part inputs, efficient variation analysis is performed, identifying the optimal joining sequences. The results for each cluster are later assembled to provide a variation analysis on the geometrical outcomes considering the optimal joining setup. The proposed method is applied on three different sheet metal assemblies, and variation analysis is performed identifying the optimal sequence. The results are then compared to the previous standard MIC approach with and without considering the joining sequences, and computation efficiency and accuracy are evaluated. The results show that the proposed method increases the computation efficiency compared to the standard MIC approach for variation analysis, considering the optimal joining sequence. The accuracy comparison shows that the proposed clustering and decomposition approach achieves more accurate results and provides potential improvements identifying the optimal joining sequences for variation simulation of the batch of&nbsp;non-rigid assemblies.</p>

Efficient Joining Sequence Variation Analysis of Stochastic Batch Assemblies

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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