Research On Wear Life Model Of Honing Oilstone And Experiment Based On Size Prediction

Honing as the last process of parts processing,has an important impact on the machining accuracy of products.The wear state of honing oilstone can directly affect the honing process.If the oilstone is worn too fast,it will cause the product processing to fail to meet the accuracy requirements.And even lead to product scrap At present,the judgment of honing on the wear state of oilstone is in the stage of manual inspection,which is troublesome and inefficient.Therefore,this paper puts forward the method of size prediction to judge the wear state of oilstone.On the basis of studying the influencing factors of oilstone wear,it can effectively manage the life of honing oilstone by predicting the size of oilstone.The main contents and the results of this paper are as follows(1)The mechanism of material removal caused by plastic deformation and fracture of materials is briefly introduced.The wear mechanism in friction process is analyzed from the point of view of abrasive particles.And the three basic stages of material removal process and the wear form of honing are introduced.By establishing the simulation model of oilstone,the process of multi-abrasive participating in cutting is reflected(2)The effect of honing pressure,reciprocating speed and size of oilstone on oilstone wear was analyzed by response surface method.The response surface test scheme is designed,and the experiment is carried by Box-Behnken.Based on the test data,the second-order model of honing oilstone life is established.And then,the oilstone life model is fitted.The validity and significance test verify the validity of the life model.After analyzing the response surface of honing oilstone life on the influencing factors,the cross influence law of each factor on oilstone life is obtained,and the process parameters are optimized(3)The grey system theory is used to establish the grey system GM(1,1)prediction model,which is used to simulate the size prediction of oil stone wear Through the accuracy test of the model,the prediction accuracy of the model is better because of the small data sample.But for GM(1,1)model,the accumulation and subtraction of data,the equation group presents a strong ill condition,which brings difficulties to parameter estimation.In this paper,the ridge regression optimization algorithm of grey system model is used to improve the prediction accuracy(4)For GM(1,1)model,the prediction accuracy is affected by the background value.The background value coefficient of the classical grey model is 0.5,but with the increasing of sample data,the prediction accuracy of the model will decrease Therefore,this paper uses the background value weight sequence instead of the single weight of the classical model,uses the genetic algorithm to iterate and optimize,and obtains the optimized prediction model.Through the simulation calculation of honing stone size data,the simulation results show that the prediction accuracy of the optimized model is 38.63%higher than that of the classical model of GM(1,1)Through the friction experiment and prediction model verification of oilstone,the wear state of oilstone in the process of processing is studied,and the influence rule of process parameters on the life of oilstone is obtained.The prediction model can predict the wear of oilstone in advance,which is of great significance to ensure that the precision of honing products is not affected by the wear of oilstone and to replace oilstone in time.