CONTACT SEARCH USING A KD-TREE FOR NON-RIGID VARIATION SIMULATION

Paper in proceeding, 2022

Geometric variation is one of the causes of aesthetic and functional issues in mechanical assemblies. To predict the geometric variation in assemblies of rigid and non-rigid parts, statistical variation simulation is introduced. For non-rigid parts, bending and deformation occur during the assembly process. In non-rigid variation simulation, contact modeling is utilized to avoid the virtual penetration of the components in the adjacent areas. Contact modeling imposes non-linear behavior to the MIC approach for variation simulation, and thereby the problem complexity and simulation time increase. Traditionally, iterative node search is used to identify and define the computational contact nodes. However, iterative search is time-demanding, specifically in large-scale models, as the search space increases by the number of nodes included in the assembly. To allow for faster contact search, a data structuring method using Kd-trees and nearest neighbor search (NN) is implemented and integrated into a computer aided tolerancing tool, enhancing the search functionality and reducing the search time compared to iterative one-by-one node search. The method is applied to three reference assemblies of different size, and the identified contact nodes and the time needed to perform the search is compared to an iterative node search. The results show that the K-tree structure and nearest neighbor search perform considerably, 96 %, faster than the iterative node search. The method increases the search performance, while the identified contact points are similar to the ones identified by an iterative search. The approach efficiently enables the contact search of large models and reduces the modeling time required for nonrigid variation simulation.