Ball Bearing Grinder Agency Analysis Of The Grinding Process Parameters Optimization

Ball bearing is one of the rolling bearings, it is mainly used in high-speedrotation occasions, its accuracy level has a great impact on the function of manymachines. The inner ring is an important component of the ball bearing, itschannel surface is the key surface which the rolling element rolls on and transferforce to. In this paper, I take the3MZ1313Ball Bearing Grinder for the objectof study. This is a special grinder for grinding the channel surface of the ballbearing'inner ring. In this paper, the research work is listed as the followingaspects:(1) Firstly, I find out that the ball bearing grinder' mechanism has twokinematics chains and three degrees of freedom, and work up some coordinatesof the constituent components of the grinder 'mechanism model. I respectivelyset up the kinematics models of the two chains, solve the two kinematicsmodels.(2) I work up the solid model of the grinder 'mechanism by way ofPro/E3.0software, and use the motion simulation module of Pro/E3.0softwareto check the interference condition of the mechanism model. Without theinterferences, it can be imported into the software of ADAMS2005through theMECHANISM/Pro interface module, then carry out the dynamic simulation.(3) I summarize out the main factors which affect the grinding accuracy ofthe ball bearing' inner ring channel,analyze them in some ways. Next, I doin-depth analysis on the vibration models of the grinding wheel—work pieceand the grinding wheel frame in the process of grinding. Understanding howthey affect the grinding accuracy. By taking some measures,we can reduce theimpact of vibration on the grinding accuracy.(4) BP neural network has powerful nonlinear mapping function, it canwork up the model of relationship between the grinding precision and thegrinding process parameters. The neural network input and output samples areobtained in accordance with uniform design method, in this paper. I design thestructure of the network according to the actual requirements, using these samples to train network. BP neural network also has a self-learning function, itcan adapt to the changing grinding precision requirements. Finally, after theneural network is trained, a good Correspondence is formed between the inputsamples and the output samples, and achieve the optimization of processparameters.