摘要
为建立磨削加工参数与磨削力导致的力变形误差之间的关系模型,提出基于神经网络的力误差建模和实时补偿方法。建立经遗传算法优化的BP神经网络以表征磨削参数与磨削力的关系;运用有限元方法对零件进行力学分析,建立磨削力与力变形量的关系模型;建立加工参数与切削力误差映射模型,预测误差补偿量,进行实时补偿。实验结果表明:该切削力误差模型准确有效,具有较高的应用价值。
In order to establish the relationship model between grinding parameters and deformation error of workpiece caused by grinding force,force error modeling and real⁃time compensation method based on neural network and finite element simulation were pro⁃posed.A BP neural network optimized by genetic algorithm was established to characterize the relationship between grinding parameters and grinding force.Then,the mechanical analysis of the parts was carried out by using the finite element method,and the relationship model between grinding force and force deformation was established.Finally,the error mapping model of the machining parameters and cutting force was established to predict the amount of error compensation and make real⁃time compensation.Experimental results show that the error model of the cutting force is accurate and effective,and has high application value.
作者
杜柳青
刘琳
DU Liuqing;LIU Lin(College of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《机床与液压》
北大核心
2021年第4期1-5,共5页
Machine Tool & Hydraulics
基金
国家自然科学基金项目面上项目(51775074)
重庆市重点产业共性关键技术创新重点研发项目(cstc2017zdcyzdyfX0066
cstc2017zdcy-zdyfX0073)
重庆市技术创新与应用示范重点项目(cstc2018jszx-cyzdX0144)
重庆市基础研究与前沿探索项目(cstc2018jcyjAX0352)
重庆市研究生科研创新项目(CY519315
CYS19316)。
关键词
BP神经网络
遗传算法
有限元仿真
力误差补偿
BP neural network
Genetic algorithms
Finite element simulation
Force error compensation
