Simulation Of Cutting Process And Surface Roughness Study Of SiCp/Al Composites

Silicon carbide reinforced aluminum matrix composites have excellent physical and mechanical properties and are widely used in aerospace,automotive,electronics and other fields.In the process of cutting,due to the presence of SiC particles,SiCp/Al composite workpiece has been processed surface often appear pits,holes,micro-cracks and matrix tear defects,while the tool will also occur serious wear,seriously restrict the promotion and application of SiCp/Al composite materials,Therefore,it is necessary to study the processing technology of SiCp/Al composites.In this paper,the finite element method is used to simulate the cutting process of SiCp/Al composites,and the roughness of machined surface is analyzed theoretically and experimentally.The main research contents are as follows:Firstly,a two-dimensional turning simulation model was established by using ABAQUS finite element software,and the random distribution of SiC particles with different shapes,volume fractions and particle sizes in aluminum matrix was realized based on Python language.Analyzed the different particle shape,volume fraction,particle size and the knife is blunt round radius of cutting process parameters,such as cutting force,cutting mechanism,has processing surface quality and material the influence law of internal stress and strain,the interface layer reinforcement of particles is studied,simulations of particle debonding effect,and the cutting force,comparing the simulation value and experiment value error below 20%,The correctness of the model is proved.Then,the fracture forms of SiCp/Al composites are analyzed,and the influencing factors of the roughness of SiCp/Al composites are studied.A surface roughness prediction model of SiCp/Al composites is developed.In addition,Particle Swarm Optimization(PSO)and Genetic Algorithm(GA)are used to optimize the neural network model respectively,and the prediction results of the two are compared,and it is considered that the optimization effect of particle swarm optimization is better.Finally,the neural network prediction system is designed based on GUIDE in MATLAB.The system is divided into 8 modules,including login,registration,exit,data processing,parameter input,function control,output control and menu bar.The realization process of each part of the module is described in detail.By writing the callback function to meet the functional requirements of each module of the system,and the function of each module of the system is tested to verify the stability of the system.