Key Fundamental Research On Laser Assisted Machining Of Silicon Nitride Ceramics

Engineering ceramics are widely used in the fields of machinery,chemical industry,energy,aerospace,metallurgy,environmental protection and energy saving due to its high strength,high hardness,low material proportion,high chemical stability,excellent biological compatibility and low thermal expansion coefficient.It also put forward higher requirements for its processing methods at the same time.Laser-assisted machining is a technology which uses laser as heat source and high-energy laser beam to heat the local area of ceramic on the basis of conventional turning,so as to make it reach the softening temperature and have a certain size of softening layer,and then use the tool to remove the material.It can change brittle machining into plastic removal,improve tool wear,improve machining surface quality and machining efficiency.In this paper,hot pressed silicon nitride ceramics are selected for laser-assisted machining.The main research contents are as follows:Based on the theory of heat conduction,the mathematical model of temperature field in laser assisted machining of silicon nitride ceramics was established.The temperature distribution on the workpiece surface and at the center of laser spot under different laser power,cutting speed and laser spot diameter was simulated by ANSYS finite element simulation software.It was found that the isotherms on the surface of the workpiece were elliptical,the closer the isotherms were to the center of the spot,the denser the isotherms were,while the thickness of the radial softening layer was small and the temperature gradient was large.The experimental platform of laser assisted machining of silicon nitride ceramics was built.The laser heating system and cutting system were connected by the supporting device of laser heating external optical path system,so as to realize the cooperative movement of the two systems.Combined with the modeling simulation and experimental research,the simulation results of laser power were verified by the trend of surface roughness and material removal rate,and the depth of softened layer under different laser power and cutting speed was taken as the basis for selecting back draft,and the range of each process parameter was preliminarily determined.According to the orthogonal test table of four factors and four levels designed by the primary processing parameters,the influence of laser power,cutting speed,feed speed,back draft on the surface roughness,material removal rate,tool wear and surface micro morphology was studied by range and variance analysis.The primary and secondary relations of the influence on the surface roughness was: laser power,cutting speed,back draft,feed speed;the order of material removal rate affected by four factors was: back draft,cutting speed,feed speed,laser power;the size relations of the influence on tool wear was: laser power,.The chip morphology and the surface morphology corresponding to the brittle,and then the plastic and heat damaged machining states were analyzed.Due to the limitation of orthogonal test in optimizing process parameters,neural network genetic algorithm was selected to optimize the process parameters.BP neural network model was used to establish the corresponding relationship between the processing parameters and the surface roughness.Then genetic algorithm was used to optimize the test results of neural network model,optimize the process parameters and laser parameters to improve the surface quality Quantity.The results showed that the theoretical optimization result of neural network genetic algorithm was 2.63% lower than that of orthogonal test.