Study On The Key Technology Of Plunge Grinding Optimization Based On Power Signal

The rotating type parts which always work in high speed environment and are strict with machining surface quality,are widely used in mechanical machine.Compared with other grinding ways,plunge grinding is a main way for the most rotating parts due to relatively uniform wear on grinding wheel radial surface and higher machining accuracy.With the development of auto parts and aeronautics & astronautics modern industry in recent years,the requirement for workpiece quality is higher and higher,so it is necessary to optimize plunge grinding system parameters to improve machining quality and grinding efficiency.However,plunge grinding is multi-variable complex process which is affected by workpiece material characteristic,process parameters,grinding wheel,machine vibration,and lubrication condition and so on.How to monitor grinding process and optimize grinding parameter effectively is always the enterprise's difficult problem to improve market competiveness.According to the actual needs of manufacturing enterprises,a lot of research work is done to grinding monitoring and optimization technology based on grinding power signal material removal rate theory model.The detail contents are following as:Grinding material removal mechanism and system relastic deformation are researched deeply based on grinding force,monitoring signal and grinding material removal rate model.Then,a kind of general monitoring signal(power signal and acoustic emission signal)material removal rate theory model is built for multi-infeed plunge grinding process.The model which is added the factor of every infeed elastic deformation has more prediction accuracy.To determine the general model unknown coefficients,a flow diagram is provided.To verify the effectiviness of the theory model,the bearing outer race way internal plunge grinding process is as experiment research object.The experiment results show that the power signal is more adaptive to build the grinding material removal rate theory model.According to the deviation between the theory model predicted and the actual measurement power signal,an enperimental method is proposed to improve the accuracy of the theory model significantly.The improved theory model is then used to analyse grinding wheel surface performance and workpiece quality,and its praticbility and theory value is proved.For the difficult key technology problem of plunge grinding,such as grinding chatter,wheel wear and grinding surface quality monitoring and so on,the relationship between theory model and monitoring signal is built based on grinding material removal rate model.The contact stiffness monitoring method is proposed by grinding material removal rate model time constant,and the relationship model between constact stiffness and system natural frequency is built.The information rate is as the parametric variable of acoustic emission signal and vibration signal to online study grinding chatter phenomenon.The specific energy algorithm is proposed to monitor grinding wheel blunt state by using online measuring power signal.And,the grinding surface roughness and machining size error prediction method are proposed based on the relationship between grinding surface roughness,workpiece size error and grinding removal rate model.The research results provide effective solution for the key plunge grinding monitoring problem.According to the machining affectiveness of grinding infeed parameter and non-infeed parameter,a new grinding infeed parameter optimization method which is added the system elastic deformation factor,is proposed by using grinding removal rate theory model.And,the grinding non-infeed parameter optimization method is proposed based on the grey system theory.At last,the grinding system parameter optimization software is developed to improve the machine technology level,the grinding surface roughness and the machining effectiveness.The system software provides a very positive support to accelerate the development of plunge grinding process optimization and to enhance enterprise market competitiveness.