doi:10.16186/j.cnki.1673-9787.2020060052

Received:2020/06/17

Revised:2020/09/15

Published:2021/09/15

Surface roughness prediction of ultrasonic extrusion processing based on MEA-BP neural network

CHEN Shuang, ZHANG Zhi, XIAO Jinchu, HU Jiajin, ZHAO Ludong

College of Mechanical and Electrical Engineering， Jiangxi University of Science and Technology， Ganzhou 334000，Jiangxi，China

Abstract:In order to effectively predict the surface roughness of the workpiece after ultrasonic extrusion processing， a prediction model was established with rotational speed ，feed speed ， amplitude ， extrusion pressure and extrusion times as input parameters and surface roughness as output parameters. The weights and thresholds of BP neural network were optimized in this model by using the global search ability of mind evolutionary algorithm（MEA）. In order to demonstrate the effectiveness of the model， BP neural network was used to predict 45steel after ultrasonic extrusion processing. The weights and thresholds of BP neural network were optimized by introducing mind evolutionary algorithm（MEA） and genetic algorithm（GA） ，and the prediction accuracy of the three models was compared and analyzed. The results showed that the prediction model of MEA-BP was the most accurate under the same experimental conditions. Compared with BP neural network ， the proposed model had higher accuracy and faster running speed.

Key words:ultrasonic extrusion processing;surface roughness prediction;mind evolutionary algorithm;BP neural network;prediction accuracy

 基于MEA-BP神经网络的超声挤压加工表面粗糙度预测_陈爽.pdf