单向CFRP螺旋铣削力建模

万敏; 杜宇轩; 张卫红; 杨昀

doi:10.7527/S1000-6893.2020.24134

航空学报 >

2021 , Vol. 42 >Issue 10: 524134 - 524134

DOI: https://doi.org/10.7527/S1000-6893.2020.24134

论文

单向CFRP螺旋铣削力建模

万敏 ,
杜宇轩 ,
张卫红 ,
杨昀

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西北工业大学机电学院, 西安 710072

收稿日期: 2020-04-24

修回日期: 2020-05-09

网络出版日期: 2020-06-12

基金资助

国家自然科学基金（51675440，51705427）

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Cutting force modeling in helical milling process of unidirectional CFRP

WAN Min ,
DU Yuxuan ,
ZHANG Weihong ,
YANG Yun

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School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2020-04-24

Revised date: 2020-05-09

Online published: 2020-06-12

Supported by

National Natural Science Foundation of China(51675440,51705427)

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摘要

螺旋铣削加工工艺具有降低轴向力，改善排屑、散热条件等优点，螺旋铣削力是其重要过程指标之一。对单向CFRP螺旋铣削力建模方法展开研究，预测给定加工参数下的螺旋铣削力。首先，通过对螺旋铣削过程进行运动学分析和切屑几何分析，建立了螺旋铣削过程中侧刃、底刃动态切屑层模型，纤维切削方向角度模型和动态切削力计算模型。然后，分别通过侧刃直线槽铣实验和底刃半齿插铣实验，对各个切削方向角度下侧刃、底刃切削力系数进行了标定，并利用人工神经网络对切削力系数进行拟合。最后，将标定所得的切削力系数代入动态切削力计算模型中，建立了单向CFRP螺旋铣削过程动态切削力预测模型，并通过实验验证了模型的准确性。与现有模型相比，该模型不仅能够预测刀具螺旋运动周期内的切削力变化情况，还可以对每个刀具自转周期内的细节进行预测，通过考虑纤维切削方向角度对切削力系数的影响，反映了单向CFRP材料的各向异性，较为准确地预测了螺旋铣削力。

关键词： 纤维增强复合材料; 螺旋铣; 切削力建模; 纤维切削方向角; 人工神经网络

本文引用格式

万敏 , 杜宇轩 , 张卫红 , 杨昀 . 单向CFRP螺旋铣削力建模[J]. 航空学报, 2021 , 42(10) : 524134 -524134 . DOI: 10.7527/S1000-6893.2020.24134

Abstract

The helical milling process has the advantages of reducing the axial force and improving the chip removal and heat dissipation conditions. One of the important process indicators is the helical milling force. In this paper, the modeling method of unidirectional CFRP helical milling force is studied to predict the helical milling force with given machining parameters. First of all, through kinematics analysis and chip geometry analysis of the helical milling process, the side edge and bottom edge dynamic chip thickness models, the fiber cutting direction angle model, and the dynamic cutting force calculation model of the process are established. The cutting force coefficients are then calibrated through the linear groove milling experiment and the bottom edge half-teeth gear milling experiment, respectively, and fitted by the artificial neural network. Finally, the calibrated cutting force coefficients are introduced into the dynamic cutting force prediction model, thereby establishing the unidirectional CFRP helical milling dynamic cutting force prediction model. The accuracy of this model is subsequently verified through the experiment. Compared with the existing model, this model can predict both the change of the cutting force in the spiral motion cycle and the details of each tool rotation cycle. Considering the influence of the fiber cutting direction angle on the cutting force coefficient, it reflects the anisotropy of the unidirectional CFRP material, therefore more accurately predicting the spiral milling force.

Key words： fiber reinforced composites; helical milling; cutting force modeling; fiber cutting direction angles; artificial neural networks

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