DocumentCode :
2944184
Title :
Research in Reliability Modeling of WEDM Based on Neural Network
Author :
Zhang Hong-bin ; Jia Zhi-xin
Author_Institution :
Sch. of Mech. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
Volume :
3
fYear :
2009
fDate :
11-12 April 2009
Firstpage :
277
Lastpage :
280
Abstract :
Because of the small size of the wire-electronic discharging machine (WEDM) reliability data, the reliability model of WEDM can not be set up accurately by the traditional reliability modeling methods. Therefore, the neural network is used in this paper to solve this problem. The algebraic algorithm, a new algorithm of neural network, is used in the proposed method. Based on statistically analyzing the reliability data of WEDM, the general idea and basic method is proposed for setting up reliability model by neural network, and a new way of reliability modeling is found out for WEDM reliability modeling. After an example is calculated, it is shown that the calculation time of the proposed method is 3 times, and the mean absolute error of the proposed method is 0.0156, as well as the mean absolute error of the traditional method is 0.0703. It is proved that the model which is set up by neural network can map the actual data more accurate than the traditional model. It has a good reference to WEDM reliability researching.
Keywords :
algorithm theory; cables (electric); electrical discharge machining; neural nets; reliability theory; WEDM; algebraic algorithm; mean absolute error; neural network; reliability data; wire-electronic discharging machine; Accreditation; Artificial neural networks; Automation; Biological system modeling; Data analysis; Mechanical engineering; Mechatronics; Neural networks; Predictive models; Uncertainty; WEDM; algebraic algorithm; neural networ; reliability modeling;
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.2009.20
Filename :
5203200
Link To Document :
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