Simulation Of Two-Parametric Selective Assembly Of Two Parts

Abstract

The article addresses the issue of ensuring the accuracy of the mating of parts in precision products through the application of multi-parameter selective assembly. A case is presented involving the formation of assembly sets consisting of two parts that are simultaneously mated to each other based on two parameters, which may include various physical quantities (geometric, mechanical, electrical, etc.). To evaluate the indicators of assembly processes, including selective assembly, analytical and simulation modeling methods are traditionally used. The aim of this work is to compare, using a specific example, the results of analytical and simulation modeling of two-parameter selective assembly of two parts in determining the total number of assembly sets, both with and without considering sorting errors. The main analytical dependencies are provided to determine the probability of forming assembly sets, as well as a generalized structure of the simulation model implemented in GPSS World. As an example, the process of selective assembly of a precision unit consisting of two parts is examined, while simultaneously ensuring the accuracy of the connection based on two parameters (linear dimensions). The parameters and measurement errors are independent random variables with known distribution laws. A scheme for the arrangement of tolerance intervals is accepted, and both extended and group tolerances are known, as well as the number of selective groups and the limits of the random components of measurement errors. Under the established grouping rules, the deviation in simulation results for both options does not exceed 0.3%, indicating a high degree of conformity between them. Utilizing the analytical model to determine the number of assembly sets significantly reduces simulation time while maintaining high accuracy of results. Further research is planned to conduct a series of multifactorial experiments to assess the correspondence of these models when a greater number of influencing factors are changed.