Wheel well bushing is a kind of high-precision thin-wall parts.A method of machining quality prediction based on multi-output deep neural network model is proposed according to existing machining data.Firstly,the data pro-cessing,selection and correlation analysis of the existing wheel well bushing processing data are carried out to identify the main variables affecting the machining quality.Then,the turning spindle speed,turning feed speed,grinding spindle speed,grinding feed speed and aging time were taken as the processing parameters,and the inner diameter,outer diameter and roundness of the bushing were taken as the output for the model training,and tested on 150 sets of wheel well bushing data.The results show that the accuracy errors of the proposed model are 0.202%,0.254%and 0.274%respectively in the inner diameter,outer diameter and roundness of the bushing.The trained model can quickly predict the machining quality of the finished product,avoid the errors caused by manual experience parameters,and improve the product quality.
machine learningdeep neural networkwheel well bushingmultiple output regression
NETL
NSTL
万方数据
