Simulation Analysis Of Abrasive Belt Wear And Study About The Influence Of Material Removal

With the gradual development of China’s manufacturing 2025-Strategy,The role of China is gradually changing from a manufacturing country to a manufacturing power-country.In high precision manufacturing,the manufacturing technology capabilities of free-form blades show the level of national industrial capabilities.In order to improve machining efficiency,to reduce labor costs,many companies gradually use CNC belt grinding machine,so belt grinding technology has been widely developed.For abrasive belt grinding,the working state of the abrasive particles on the surface of the abrasive belt will directly affect the processing quality and efficiency.The abrasive particle grinding process is a microscopic multi-particle concurrent-cutting process.Its action phenomenon and parameters are difficult to obtain by conventional means such as observation and measurement.Therefore,the finite element micro simulation technology is used to simulate the working mechanism of a single abrasive particle,and analyze various process parameters such as grinding speed,grinding depth,inclination angle and the radius of partice.According to the difference in the degree of influence of different parameters,it is important to improve the rationality of the experimental parameter selection in the experiment.The abrasive wear of abrasive belts is a complicated process.As the process progresses,the wear patterns gradually change.Based on ABAQUS,micro-simulation experiments are used to obtain the required parameters such as the temperature and contact pressure of the abrasive particles in the working state,and then Python is used to carry out the secondary development of the subroutines before and after processing,and the simulation results will be extracted.The parameters are combined with the mathematical model of wear to obtain the changes of the discrete nodes of the abrasive finite element,and then a shape updating logic is used to approximate the wear process of the abrasive grains,which is conducive to the in-depth understanding of the wear process of the abrasive particles.Based on regression prediction-BP neural network algorithm,experiments are performed through different combinations of experimental parameters,and BP neural network training is performed with a certain amount of data,and various process parameter combinations such as grinding speed,contact pressure,workpiece material,etc.and wearing-rate are established.Through this artificial intelligence method,the purpose of predicting abrasive belt wear more quickly and accurately is achieved,and the processing process is guided,which greatly improves the processing efficiency.In addition,when using abrasive belts with different wear levels for grinding,it will have a certain effect on the surface morphology and roughness of the workpiece.Therefore,it is of practical significance to explore the degree of abrasive belt wear to improve the surface quality.Establish the relationship between abrasive belt wear and material removal rate.In the process of abrasive belt grinding,it is necessary to inspect the workpiece.But after processing is suspended.The decrease in the removal rate will lead to an increase in the detection frequency and increase the inspection of work-hours.As a result,the processing efficiency will decrease.This highlights the necessity of predicting the degree of change in the removal rate due to abrasive belt wear.Only by understanding the change in the removal rate we can control the adjustment of the residence time in the process.When exploring the relationship between belt wear and material removal rate,3D reverse engineering technology and FA trajectory planning algorithm are used to improve the efficiency of experimental detection and reduce the cost of experimental detection time.