In order to reduce the surface roughness of cold roll-beating spline and to get the optimal parameter combination, rotating speed of roller revolution and feeding rate of workpiece two main factors affecting surface roughness as variables, the cold roll-beating spline and experimental project are designed. Surface roughness of cold roll-beating spline's pitch circle was measured through white light copolymerization interference microscope. The Back Propagation (BP) neural network prediction model for surface roughness of cold roll-beating spline was established based on the experimental data through trial and error method. The optimal neural network structure 2-6-2-1 was determined. The predicted values and the training samples and testing samples were contrasted and analyzed. The results show that the maximum error between the predicted values and the training sample is 6.5% and the maximum error between the predicted values and the training sample is 7.9%. The correlation coefficient between the predicted values and the training samples is 0.996 and the correlation coefficient between the predicted values and the testing samples is 0.973. The validity and accuracy of neural network prediction model are further illustrated.
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王晓强,刘佳,卜敏,韩坤鹏.冷滚打花键表面粗糙度神经网络预测模型建立[J].机床与液压,2017,45(17):99-104. . Establish Neural Network Predictive Model for Surface Roughness of Cold Roll-beating[J]. Machine Tool & Hydraulics,2017,45(17):99-104