Research On Finite Element Analysis And Experiment Of Drilling Of Carbon Fiber Reinforced Polymer Composites

Carbon Fiber Reinforced Polymer Composites(CFRP)has been widely applied in aerospace,automobile,architecture,and other significant industrial fields due to its excellent performance such as light weight,high strength and anti-fatigue.Drilling is a common hole making process in mechanical processing,and the hole quality has a great influence on the assembly precision and reliability of the parts.However,due to the properties of high hardness,anisotropy and low interlayer bonding strength of CFRP,as well as the unreasonable cutting parameters and geometric parameters of the cutting tools,it may cause quality defects such as delamination,burr,tearing and so on in the process of drilling,which may lead to reduce the assembly accuracy and structural strength of CFRP parts and increasing the processing cost.Therefore,it has become an important research topic in the field of manufacturing to realize the goal of high quality,high efficiency and low cost in drilling CFRP in recent years.The simulation model of drilling CFRP is established.The accuracy of the simulation model is verified by comparing the simulation model with the experimental research in the related literature.The spindle speed,feed rate and drill angle are used as the design variables.The drilling simulation test of CFRP were conducted based on response surface method and Box-Behnken test design.The prediction model of thrust force in drilling CFRP is constructed and the effect of the three variables on the thrust force is studied synthetically according to the response surface and contour map.Moreover,the drilling simulation test of CFRP are carried out based on the orthogonal test method.The effects of three variables on the delamination factor are analyzed by using the range analysis and ANOVA analysis.The artificial neural network prediction model and multiple linear regression prediction model of delamination factor are established.By making use of the nonlinear mapping ability of neural networks,coupled with the global searching ability of genetic algorithms,an optimal combination of drilling parameters giving the minimum delamination factor is obtained.The results show that the thrust force and delamination factor decreases with the increase of the spindle speed,and increases with the increase of the feed rate and point angle.The order of influencing significance on the delamination factor is the feed rate,spindle speed,and point angle.The maximum relative errors of the prediction models using artificial neural network and multiple linear regression method are respectively 3.1% and 7.4%.It is suitable to increase spindle speed,reduce feed rate and drill angle in practical machining process,so as to reduce the delamination,tearing and other defects and further improve the quality of hole making.Moreover,the method used in this paper also provides a theoretical reference for the study of the cutting temperature and tool wear in drilling CFRP.