Author/Authors :
Zhang,Bin The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body - Hunan University, China , Gong,Jinke The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body - Hunan University, China , Yuan, Wenhua Department of Mechanical and Energy Engineering - Shaoyang University, China , Fu, Jun Department of Mechanical and Energy Engineering - Shaoyang University, China , Huang, Yi The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body - Hunan University, China
Abstract :
In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.
