Research On Selection And Matching Of Bearing Components Based On Rotation Accuracy Prediction

Bearings are one of the most commonly used parts in the machinery industry.Its accuracy and performance are the basis for the performance guarantee of all mechanical systems.In the bearing production,the bearing assembly is an important process that has a huge impact on the rotation accuracy and assembly accuracy of the finished bearing.However,in the current bearing assembly process,the assembly is mainly based on the radial clearance and the fitting rate.To a certain extent,the rotation accuracy of the bearing is ignored,and there is more manual participation,which makes it difficult to realize automatic production.In order to solve the above problems,this article takes deep groove ball bearings as an example.Under the background of automatic bearing production,a new method for selecting and matching bearing components is proposed.The bearing components are selected and matched with the goal of bearing rotation accuracy and clearance.The specific research content is as follows:(1)Establish a numerical model of the slewing accuracy of deep groove ball bearings.Considering the relationship of the micro-geometric structure of the bearing components,a geometric model of the bearing motion with the outer ring fixed and the inner ring moving is established,and by establishing the bearing mechanical balance equation,the runout amount in the three-dimensional space of the bearing inner ring is solved.(2)Taking the model 6312 deep groove ball bearing as the experimental object,the necessary dimensions of each bearing element are detected and substituted into the rotation accuracy model to obtain the theoretical results.Then,the radial runout and axial runout of the inner ring of the complete set of bearings after the assembly are detected,and the theoretical results are compared.The validity of the slewing accuracy model is verified as the basic theory for the establishment of the selection model of bearing components.(3)Based on the numerical model of bearing rotation accuracy,two sorting schemes for bearing components are established.One is to meet the clearance requirements and the rotation accuracy requirements as constraints(Condition matching),and the other is to meet the clearance requirements and the highest rotation accuracy as the constraints(Optimized matching).(4)In order to meet the tempo requirements of the bearing automation production line,the BP neural network algorithm is used to optimize the calculation program of the matching scheme,which improves the calculation efficiency of bearing matching.(5)A case analysis is carried out,and the results show that bearing assembly according to the matching method proposed in this paper can improve the fitting rate and rotation accuracy of the assembled bearing;after the optimization of the BP neural network algorithm,the matching calculation time can meet the requirements of the production line.The research work in this paper provides a feasible method for the selection and matching process of bearing components in the automatic production of bearings,and lays the foundation for the construction of the automatic production line of bearings.