Analysis of Assembly Tolerance Based on Assembly Constraint Information Model

Abstract

Mechanical products are composed of two or more parts. The geometric tolerance and dimensional tolerance of each feature in part will affect the assembly performance of the product, which are accumulated and propagated between assembly fit and parts. In this paper, through the secondary development of CAD software, the B-rep model of parts is obtained. The model information is decomposed and simplified based on geometric features to obtain the key information of parts in the assembly process, simplify the operation, and improve the accuracy. Through a directed graph network, the transmission model of assembly error information based on geometrical and dimensional tolerances (GD&T) on the surface of parts is established. Combined with the error transfer characteristics of different geometric surfaces and different error sources, guided by the breadth-first search algorithm and the shortest path theory, the search and establishment of a three-dimensional assembly chain are realized. Finally, the three-dimensional chain is simulated by the Monte Carlo method. The calculation results are compared with the error range obtained by the traditional method to prove the effectiveness of the method.

Tolerance analysis by static analogy: numerical and experimental results

“…For each tolerance, a probability density function was defined to run Monte Carlo simulations and to solve Eq. (17) .…”

“…A new method based on univariate dimension reduction and the Pearson system was developed to overcome the normality assumptions for all dimensions. 17 It takes into account only the dimensions. A classification of tolerance analysis issues is reported in, 18 based on the kind of uncertainty considered.…”

Critical component detection in assemblies: a graph centrality approach

“…Studies such as: (Gunji et al, 2022), (Watson and Hermans, 2019) and (Kosec et al, 2020) have utilised graphs to model assembly sequences and their assemblability, with the latter integrating tolerance analysis via error propagation. Innovative applications such as using knowledge graphs for process planning (Zhou et al, 2022), kinematic modelling of a configurable assembly cell (Zhang et al, 2022) and the generation of graphs from boundary representation models that incorporate error transfer and tolerances (Li and Hou, 2021), exemplify the evolving role of graph theory in this field. Furthermore, the concept of knowledge graphs (see Akroyd et al, 2021) has also been used to build a semantic understanding of systems, aiming to develop comprehensive Digital Twins.…”