基于改进混沌粒子群算法的薄壁件铣削参数优化

为提高薄壁框体结构件铣削加工精度及加工效率，提出一种薄壁框体结构件铣削加工工艺参数 优化方法。针对标准粒子群算法存在易陷入局部最优解，且不能自适应调整权重系数等问题，将混沌算法与多 目标粒子群算法结合，建立了以铣削力和单位时间材料去除率为优化目标，以铣削 4 因素为优化变量，以机床 主轴转速、进给量、铣削深度和表面粗糙度为约束条件的多目标约束优化模型。利用有限元仿真准确计算每个 优化解的加工误差，将结果及时反馈到优化算法中，进而找到最优加工工艺参数组合。以典型薄壁结构侧壁铣 削为例，分别采用试验参数、标准粒子群优化参数和本文所提算法优化结果进行仿真模拟，对仿真结果进行分 析比较，证明了该方法的有效性。

Optimization of milling parameters for thin-walled parts based on improved chaotic particle swarm optimization algorithm

In order to improve milling precision and processing efficiency of thin-walled frame structural parts, a method for optimizing the milling processing parameters of thin-walled frame structural parts was proposed. Aiming at the problems of standard particle swarm algorithm that are easy to fall into local optimal solutions and cannot adjust weight coefficients adaptively, this method combined chaos optimization algorithm and multi-objective particle swarm optimization algorithm to establish the optimization target based on milling force and material removal rate per unit time. The four factors of milling were taken as optimization variables, and the spindle speed, feed rate, milling depth, and surface roughness were taken as constraints. The machining error of each optimization solution was calculated accurately by finite element simulation, and the results were fed back to the optimization algorithm in time, so as to find the optimal machining parameter combination. Taking typical thin-walled structure sidewall milling as an example, experimental parameters, standard particle swarm optimization parameters, and optimization results of the algorithm proposed in this paper were used for simulation respectively, and the simulation results were analyzed and compared, which proves the effectiveness of the proposed method.