Research on tolerance optimization method for aircraft thin-walled components based on improved particle swarm optimization algorithm

Abstract The assembly accuracy of aircraft thin-walled components produced in the same batch is subject to fluctuations influenced by part tolerances and assembly processes. Significant variations in assembly precision can lead to increased assembly adjustment costs, severely affecting both efficiency and overall expenses. However, tolerance optimization that takes into account these fluctuations requires the integration of assembly deviation calculations, characterized by a high demand for large sample sizes and frequent iterative computations, which results in escalated computational costs. In this context, this paper establishes a data-driven mathematical model for tolerance optimization that comprehensively considers fluctuations in assembly accuracy, manufacturing costs, and quality losses, using a surrogate model as the core computational engine. Furthermore, to address the shortcomings of the traditional particle swarm optimization algorithm, namely, its tendency to get trapped in local optima and its insufficient local search capabilities, this study proposes an improved particle swarm optimization algorithm that integrates a chaotic local search strategy and second vibration particles. This enhanced algorithm is applied to the tolerance optimization problem of aircraft thin-walled components.