Title: Energy consumption prediction model of plane grinder processing system based on BP neural network

Authors: Yan Zhou; Hua Zhang; Wei Yan; Feng Ma; Gongfa Li; Wentao Cheng

Addresses: Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Green Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Green Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Green Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Green Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Green Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan, 430081, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Hubei, Wuhan, 430081, China; Green Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan, 430081, China

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.

Keywords: processing system energy consumption; process parameters; BP neural network; prediction model.

DOI: 10.1504/IJWMC.2018.093857

International Journal of Wireless and Mobile Computing, 2018 Vol.14 No.4, pp.320 - 327

Received: 24 Jan 2018
Accepted: 08 Mar 2018
Published online: 07 Aug 2018 *

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