BP neural network model for material distribution prediction based on variable amplitude anti-blocking screening DEM simulations

Abstract

The material feeding changing of combine harvester is easy to cause accumulation and blockage of the vibrating screen, which seriously affects the harvest operation. In order to alleviate such accumulation and blockages on the vibrating screen surface, the guide chute rotation angle of the improved variable amplitude screening mechanism was selected as the target variable, and EDEM-RecurDyn was employed to simulate the anti-blocking process of the variable amplitude under a changing feeding quantity (0.5 kg/s abnormal, 0.2 kg/s normal) of materials (rice grain and stem mixture). A BP (an error back propagation algorithm) neural network was designed and the prediction model of the material distribution was subsequently constructed on the variable screening surface under different chute angles during abnormal feeding. The results revealed a continuous decrease in the quality and time of the material blockage at the front end of the screen surface with the increasing guide chute angle. At the guide chute angle of 20°-45° and adjustment time of 3-6 s, the blocked and accumulated materials at the front-end screen surface was be moved back to Grid 6 for screening. However, overtime, the screen surface materials continued to move back under the chute angle of 40°-45°, which had a great impact on the screening performance. At the guide chute angle of 30°-35° and adjustment time of 4 s, the materials on the screen surface were evenly distributed in Grid 1-6. This was able to alleviate the accumulation and blockage of the screen surface materials. The R of the material distribution prediction model (BP neural network) on the screen surface was determined as 0.97, indicating the high reliability and accuracy of the material distribution model on the screen surface based on the BP neural network. This work provides an important reference for the variable amplitude intelligent control of screen surface material anti-blocking. Keywords: variable amplitude, material distribution, EDEM-RecurDyn, BP neural network DOI: 10.25165/j.ijabe.20231604.7178 Citation: Ma Z, Zhu Y L, Wu Z P, Traore S N, Chen D, Xing L C. BP neural network model for material distribution prediction based on variable amplitude anti-blocking screening DEM simulations. Int J Agric & Biol Eng, 2023; 16(4): 191－200