Title of Paper:Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm

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Affiliation of Author(s):机电学院

Journal:INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Key Words:Grinding temperature Particle swarm optimization BP neural network Titanium matrix composites

Abstract:High grinding temperature is the key reason of workpiece burnout, which hinders the improvement of the machining quality. In this work, the prediction of high-speed grinding temperature of titanium matrix composites is investigated using back propagation (BP) neural network based on particle swarm optimization (PSO) algorithm (also called as PSO-BP). Furthermore, a comparison has been carried out among GD-BP (the BP neural network trained with gradient descent method), LM-BP (the BP neural network trained with Levenberg-Marquardt (LM) algorithm), and PSO-BP. Results obtained show that the PSO-BP method has a more significant advantage in terms of convergence speed, fitting accuracy, and prediction accuracy than the other two methods (such as GD-BP and LM-BP) in predicting the grinding temperature. Accordingly, the grinding temperature is predicted by applying the PSO-BP method and the grinding parameters are optimized, which could avoid the burnout behavior of the titanium matrix composites.

ISSN No.:0268-3768

Translation or Not:no

Date of Publication:2017-03-01

Co-author:Liu, Chaojie,Zhengminqing Li,Yang, Changyong

Correspondence Author:dwf