Mechanical property parameters prediction of tube based on RBF neural network

Meihui Jia; Chengtong Tang; Jianhua Liu; Tian Zhang

doi:10.4028/www.scientific.net/AMM.300-301.882

Mechanical property parameters prediction of tube based on RBF neural network

Meihui Jia^*, Chengtong Tang, Jianhua Liu, Tian Zhang

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Mechatronics and Applied Mechanics II
Pages	882-888
Number of pages	7
DOIs	https://doi.org/10.4028/www.scientific.net/AMM.300-301.882
Publication status	Published - 2013
Event	2nd International Conference on Mechatronics and Applied Mechanics, ICMAM 2012 - , Taiwan, Province of China Duration: 8 Dec 2012 → 9 Dec 2012

Publication series

Name	Applied Mechanics and Materials
Volume	300-301
ISSN (Print)	1660-9336
ISSN (Electronic)	1662-7482

Conference

Conference	2nd International Conference on Mechatronics and Applied Mechanics, ICMAM 2012
Country/Territory	Taiwan, Province of China
Period	8/12/12 → 9/12/12

Keywords

Mechanical properties parameters
Parameters prediction
RBF neural network
Tube tensile

Access to Document

10.4028/www.scientific.net/AMM.300-301.882

Cite this

@inproceedings{06d003354c084013b9521b02e3b6597e,

title = "Mechanical property parameters prediction of tube based on RBF neural network",

abstract = "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.",

keywords = "Mechanical properties parameters, Parameters prediction, RBF neural network, Tube tensile",

author = "Meihui Jia and Chengtong Tang and Jianhua Liu and Tian Zhang",

year = "2013",

doi = "10.4028/www.scientific.net/AMM.300-301.882",

language = "English",

isbn = "9783037856512",

series = "Applied Mechanics and Materials",

pages = "882--888",

booktitle = "Mechatronics and Applied Mechanics II",

note = "2nd International Conference on Mechatronics and Applied Mechanics, ICMAM 2012 ; Conference date: 08-12-2012 Through 09-12-2012",

}

Jia, M, Tang, C, Liu, J & Zhang, T 2013, Mechanical property parameters prediction of tube based on RBF neural network. in Mechatronics and Applied Mechanics II. Applied Mechanics and Materials, vol. 300-301, pp. 882-888, 2nd International Conference on Mechatronics and Applied Mechanics, ICMAM 2012, Taiwan, Province of China, 8/12/12. https://doi.org/10.4028/www.scientific.net/AMM.300-301.882

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 -

Mechanical property parameters prediction of tube based on RBF neural network

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Keywords

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