CFRP套料钻制孔工艺及轴向力预测研究

Study on drilling technology and axial force prediction of CFRP

在钻削碳纤维复合材料(CFRP)时,由钻头轴向力引起的分层损伤是影响零件装配质量与使用寿命的最重要因素之一。为了提高CFRP制孔质量,本文通过正交实验分析了主轴转速、进给量与刃径对套料钻轴向力的影响,并基于响应面法(RSM)建立了轴向力回归预测模型。针对模型精度问题,提出了一种粒子群(PSO)算法优化的径向基神经网络(RBF)预测模型,并进行了实验验证。结果表明:各主因素对轴向力的影响顺序为进给量>主轴转速>刃径,套料钻高转速、低进给、大刃径的搭配能获得更小的轴向力;PSO-RBF神经网络预测模型的平均绝对百分误差为3.27%,相对标准RBF神经网络预测模型与RSM回归预测模型误差分别降低了32.85%与44.67%,因此PSO-RBF神经网络模型能更有效地预测套料钻钻削过程中的轴向力。

When drilling carbon fiber composites(CFRP),the delamination damage caused by the bit axial force is one of the most important factors affecting the assembly quality and service life of parts.In order to improve the quality of CFRP hole making,this paper analyzes the influence of spindle speed,feed rate and edge diameter on the axial force of casing drill through orthogonal experiment,and establishes the regression prediction model of axial force based on response surface method(RSM).Aiming at the problem of model accuracy,a radial basis neural network(RBF)prediction model optimized by particle swarm optimization(PSO)algorithm was proposed and verified by experiments.The results show that the main factors influence the axial force in the order of feed rate>spindle speed>aperture.The combination of high speed,low feed rate and large blade diameter can obtain smaller axial force.The average relative error of the PSO-RBF neural network prediction model is 3.27%,which is 32.85%and 44.67%lower than that of the standard RBF neural network prediction model and RSM regression prediction model,respectively.Therefore,the PSO-RBF neural network model can predict the axial force in the process of casing drilling more effectively.