考虑刀具底刃切削的CFRP铣削力精确建模研究  

Research on Precise Modeling of CFRP Milling Force Considering Bottom Edge of Tool

作　　者：庆华楠 和延立[1] 张圣光 朱胜伟 王大振 Qing Hua＇nan, He Yanli , Zhang Shengguang, Zhu Shengwei, Wang Dazhen(Key Laboratory of Contemporary Design ＆ Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi＇an 710072, Chin)

机构地区：[1]西北工业大学现代设计与集成制造技术教育部重点实验室,西安710072

出　　处：《机械科学与技术》2018年第4期560-567,共8页Mechanical Science and Technology for Aerospace Engineering

基　　金：西北工业大学基础研究基金项目(3102015JCS05009)资助

摘　　要：针对碳纤维复合材料(CFRP)铣削提出了一种切削力精确建模方法,即考虑刀具底刃切削作用的铣削力机械模型,通过实验识别底刃和侧刃的切削力并分析了切削力变化规律,建立了切削力系数关于瞬时切削厚度、纤维切削角及切削速度的BP神经网络模型,进一步实现了对铣削力的预测。单向板和多向板的铣削验证实验表明考虑刀具底刃因素可以提高切削力预测的准确性,同时也验证了BP神经网络在CFRP切削力建模中的可行性。A mechanistic based cutting force model was proposed for predicting forces in milling of carbon fiber reinforced polymer composite（ CFRP）,in which the effects of the cutting edges on the tool periphery and bottom were both considered. Through experiments,the force contributions by peripheral edges and bottom edges were recognized and analyzed. The influences of the instantaneous chip thickness,fiber cutting angle and cutting speed on the specific cutting forces were recognized and modeled based on BP neural network. The proposed approach shows better agreement with the experimental. The result shows the prediction accuracy of force can be improved when the bottom of cutter blade was incorporated in the model. The feasibility of BP neural network for cutting force modeling was also verified.

关 键 词：铣削力预测 纤维切削角 铣削力建模 BP神经网络 

分 类 号：TG501[金属学及工艺—金属切削加工及机床]