The Application of BP Neural Network Model of DNA-Based Genetic Algorithm to Monitor Cutting Tool Wear

Author

Nie Shu-zhi ; Ye Bang-yan

Author_Institution

Sch. of Mech. & Automotive Eng., South China Univ. of Technol., Guangzhou, China

Volume

3

fYear

2009

fDate

11-12 April 2009

Firstpage

338

Lastpage

341

Abstract

This paper proposes a method of applying BP neural network model of DNA-based genetic algorithm to monitor and forecast cutting tool wear. Through the optimization by training, that is adopts DNA genetic algorithm to optimize the initial figure of BP neural networks and increases the speed of convergence and avoid local minimum, the BP neural network model can effectively extract the characteristic parameters that affect the tool wear characteristics , monitor and forecast of the tool wear, as well as get higher forecast accuracy.

Keywords

backpropagation; condition monitoring; cutting; cutting tools; genetic algorithms; machining; neural nets; precision engineering; wear; BP neural network model; DNA-based genetic algorithm; convergence; cutting tool wear forecasting; cutting tool wear monitoring; machining technology; optimization; tool wear forecast accuracy; Automation; Automotive engineering; Cutting tools; DNA; Genetic algorithms; Machining; Monitoring; Neural networks; Predictive models; Proteins; BP neural network; DNA genetic algorithm; tool wear monitoring;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on

Conference_Location

Zhangjiajie, Hunan

Print_ISBN

978-0-7695-3583-8

Type

conf

DOI

10.1109/ICMTMA.2009.160

Filename

5203215