Assembly Accuracy Optimization of Aero-engine Section Based on Dynamic Weighting Multi-Objective Optimization Algorithm

The pose-solving method for aero-engine component docking assembly often faces challenges such as slow convergence and susceptibility to local optima when dealing with complex optimization problems involving multiple features and constraints. This paper proposes an optimized assembly pose solution method for engine sections based on an improved multi-objective optimization algorithm. The method first preprocesses the high-density point clouds obtained from 3D scanning to extract geometry such as feature points, lines, and surfaces. It builds an assembly constraint model with geometric relations and process needs. It focuses on the pose solution phase: we transform the assembly problem into a nonlinear optimization problem to minimize parallelism error, gap error, and step error. In order to solve this multi-objective problem efficiently, we propose an iterative multi- objective optimization algorithm as the optimization engine and propose a dynamic weight allocation strategy. During iteration, the strategy adaptively adjusts the weight coefficients of three error terms in the overall fitness function due to the evolution of the population and convergence of each error term, guiding the search direction and balancing the algorithm's global exploration and local exploitation ability. Our results show that instead of adopting an optimization algorithm with fixed weights and a multi-Objective optimization system with fixed weight, the proposed pose solution method based on dynamic weight multi- objective optimization algorithm achieves a high accuracy and stability of the solution and can easily and accurately produce a good pose matrix which meets challenging assembly constraints, providing a practical theoretical framework and technical support for achieving high-quality automated engine assembly.