摘要
提出了一种同时考虑几何误差和非均匀载荷的多级转子装配精度预测方法,首先,在只考虑位置和方向偏差的装配误差传递模型基础上,将非均匀载荷导致的装配变形量进行叠加,建立更加精确的多级转子装配误差传递理论模型;然后,采用有限元方法仿真得到非均匀载荷作用下的转子装配精度,以此构建装配数据集,建立KAN神经网络代理模型,进行模型训练和测试,准确预测误差传递理论模型中的关键参数,与传统的回归模型进行了对比,验证了所提代理模型在精度预测方面的优越性和可靠性;最后,以一组随机误差参数作为研究案例,对比了所提模型与经典模型的预测值;采用萤火虫算法对配合面预紧力大小进行了优化,同轴度较优化前降低了38.03%,显著提高了转子系统的装配预测精度.
A method for predicting the assembly accuracy of multi-stage rotors,which takes into account both geometric errors and non-uniform loads,was proposed.First,the assembly deformation caused by the non-uni-form load was superimposed on the classical model which only considers position and direction deviations to es-tablish a more accurate assembly error propagation model of multi-stage rotors.Then,the finite element method was used to simulate rotor assembly accuracy under non-uniform loads.The dataset was constructed by FEM,and the KAN neural network surrogate model was trained to accurately predict key parameters in the error propagation model.The proposed surrogate model was compared with the traditional regression model,and its superiority and reliability in accuracy prediction were verified.Finally,a set of random error parameters was used as a case study.The prediction values of the proposed model were compared with those of the classical model.The preload of the joint surface was optimized by the firefly algorithm,and the coaxiality was reduced by 38.03%compared with that before optimization,which significantly improved the assembly prediction accuracy of the rotor system.
作者
巩浩
王星洁
刘检华
谭征岳
GONG Hao;WANG Xingjie;LIU Jianhua;TAN Zhengyue(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China;Tangshan Research Institute,Beijing Institute of Technology,Tangshan,Hebei 063015,China;Hebei Key Laboratory of Intelligent Assembly and Detection Technology,Tangshan,Hebei 063015,China;AECC Shenyang Engine Research Institute,Shenyang,Liaoning 110015,China)
出处
《北京理工大学学报》
北大核心
2025年第10期1075-1084,共10页
Transactions of Beijing Institute of Technology
基金
国家重点研发计划项目(2022YFB3304200)
国家自然科学基金资助项目(U22A20203)。
