Application of Metallic Material Machining Based on Neural Network Predictive Theory

Author

Gong, Li-Xong ; Yang, Ming-Zhong

Author_Institution

Sch. of Mech. & Electr. Eng., Wuhan Univ. of Technol., Wuhan, China

Volume

2

fYear

2010

fDate

4-6 June 2010

Firstpage

38

Lastpage

41

Abstract

The experimental program was designed according to the character of metallic material machining, and lots of data were acquired through experiment. Then the corresponding connection of input and output parameters were building based on model constructed by artificial neural network predictive theory. Radial error after machining of metallic material can be forecasted accurately in the predictive model. Lastly, predictive value and measured value of radial error after machining were compared and analyzed. The results indicated the availability and validity of artificial neural network predictive theory.

Keywords

machining; neural nets; prediction theory; production engineering computing; machining radial error; metallic material machining; neural network predictive theory; predictive model; Artificial neural networks; Demand forecasting; Electronic mail; Frequency; Inorganic materials; Machine tools; Machining; Mathematical model; Neural networks; Predictive models; machining; metallic material; neural network; radial error;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Computing (ICIC), 2010 Third International Conference on

Conference_Location

Wuxi, Jiang Su

Print_ISBN

978-1-4244-7081-5

Electronic_ISBN

978-1-4244-7082-2

Type

conf

DOI

10.1109/ICIC.2010.103

Filename

5514105