Energy consumption prediction model of plane grinder processing system based on BP neural network Online publication date: Tue, 07-Aug-2018
by Yan Zhou; Hua Zhang; Wei Yan; Feng Ma; Gongfa Li; Wentao Cheng
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 14, No. 4, 2018
Abstract: According to the processing characteristics of high energy consumption and low efficiency of China's CNC surface grinding machine, this paper studies the influence of the process parameters on the energy consumption of the processing system, and determines the wheel speed, feed speed of worktable and grinding depth as the main parameters, in which the grinding depth has the greatest influence on energy consumption. Then, the prediction model of the energy consumption of the processing system based on BP neural network is established, and the above three main factors are used as input and the additional load loss power. After training the model, the energy consumption ratio of the grinding machine system can be predicted. The prediction results show that the accuracy of the model is high, and it can predict the energy consumption of the grinder in the process well.
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