Title :
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
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;
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
DOI :
10.1109/ICIC.2010.103
