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#br# Surface roughness prediction based on Copula
EDA optimization of BP neural network
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School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China |
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Abstract To improve the prediction accuracy of surface roughness of machined surface, the method of optimizing BP neural network was proposed by Copula estimation of distribution algorithm (EDA). The experiments of milling 45# steel were conducted by the control variable method. The main cutting force, axial force, radial force and vibration amplitude were measured on line, and the corresponding average value, standard deviation, RMS values of cutting force and vibration amplitude were obtained. The twodimensional surface roughness Ra, threedimensional roughness average Sa and RMS Sq were measured offline. The correlation analysis was carried out among cutting force component mean value, standard deviation, root mean square value, vibration amplitude and roughness. The average value of main cutting force with the largest Kendall rank correlation coefficient was selected as input variable. The average main cutting force was input into BP neural network and another BP neural network optimized by Copula EDA for training and predicting. The experimental results show that the prediction accuracies of BP neural network based on Copula EDA is higher than that of BP neural network, and the average prediction accuracy of Ra, Sa and Sq are 91.98%, 91.03% and 89.10%, respectively.
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Fund:国家科技重大专项(2013ZX04009031) |
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