TY - GEN
T1 - Mechanical property parameters prediction of tube based on RBF neural network
AU - Jia, Meihui
AU - Tang, Chengtong
AU - Liu, Jianhua
AU - Zhang, Tian
PY - 2013
Y1 - 2013
N2 - Since it is difficult to predict the mechanical property parameters of tube, the parameter prediction method is proposed, which is based on RBF neural network and tube tensile tests. The stress-strain curves of partial tube are investigated by tensile tests. Then, the sample space of a neural network is established. On this basis, the neural network input parameters and output parameters are determined, and the tube is classified according to the sizes and materials to build a layered neural network model. The comparison of Network prediction and experimental results shows that the RBF neural network can effectively predict the mechanical performance parameters of tube.
AB - Since it is difficult to predict the mechanical property parameters of tube, the parameter prediction method is proposed, which is based on RBF neural network and tube tensile tests. The stress-strain curves of partial tube are investigated by tensile tests. Then, the sample space of a neural network is established. On this basis, the neural network input parameters and output parameters are determined, and the tube is classified according to the sizes and materials to build a layered neural network model. The comparison of Network prediction and experimental results shows that the RBF neural network can effectively predict the mechanical performance parameters of tube.
KW - Mechanical properties parameters
KW - Parameters prediction
KW - RBF neural network
KW - Tube tensile
UR - http://www.scopus.com/inward/record.url?scp=84874672685&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.300-301.882
DO - 10.4028/www.scientific.net/AMM.300-301.882
M3 - Conference contribution
AN - SCOPUS:84874672685
SN - 9783037856512
T3 - Applied Mechanics and Materials
SP - 882
EP - 888
BT - Mechatronics and Applied Mechanics II
T2 - 2nd International Conference on Mechatronics and Applied Mechanics, ICMAM 2012
Y2 - 8 December 2012 through 9 December 2012
ER -