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Prediction of mechanical properties of A357 alloy using artificial neural network
Alternative TitlePrediction of mechanical properties of A357 alloy using artificial neural network
Yang Xiawei1; Zhu Jingchuan1; Nong Zhisheng1; He Dong1; Lai Zhonghong1; Liu Ying3; Liu Fawei4
2013
Source PublicationTRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA
ISSN1003-6326
Volume23Issue:3Pages:788-795
AbstractThe workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property. The mechanical properties of these workpieces depend mainly on solid-solution temperature, solid-solution time, artificial aging temperature and artificial aging time. An artificial neural network (ANN) model with a back-propagation (BP) algorithm was used to predict mechanical properties of A357 alloy, and the effects of heat treatment processes on mechanical behavior of this alloy were studied. The results show that this BP model is able to predict the mechanical properties with a high accuracy. This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy. Isograms of ultimate tensile strength and elongation were drawn in the same picture, which are very helpful to understand the relationship among aging parameters, ultimate tensile strength and elongation.
Other AbstractThe workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property. The mechanical properties of these workpieces depend mainly on solid-solution-temperature, solid-solution time, artificial aging temperature and artificial aging time. An artificial neural network (ANN) model with a back-propagation (BP) algorithm was used to predict mechanical properties of A357 alloy, and the effects of heat treatment processes on mechanical behavior of this alloy were studied. The results show that this BP model is able to predict the mechanical properties with a high accuracy. This model was used to reflect-the-influence-of heat treatments on-the-mechanical-properties of A357 alloy. Isograms of ultimate tensile strength and elongation were drawn in the same picture, which are very helpful to understand the relationship among aging parameters, ultimate tensile strength and elongation.
KeywordALUMINUM-ALLOY PARAMETERS A357 alloy mechanical properties artificial neural network heat treatment parameters
Indexed ByCSCD
Language英语
CSCD IDCSCD:4809895
Citation statistics
Cited Times:6[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/146957
Collection中国科学院金属研究所
Affiliation1.Harbin Institute Technol, Natl Key Lab Precis Hot Proc Met, Harbin 150001, Peoples R China
2.Harbin Institute Technol, Sch Mat Sci & Engn, Harbin 150001, Peoples R China
3.Beijing Hangxing Machine Mfg Co, Beijing 100013, Peoples R China
4.中国科学院金属研究所
Recommended Citation
GB/T 7714
Yang Xiawei,Zhu Jingchuan,Nong Zhisheng,et al. Prediction of mechanical properties of A357 alloy using artificial neural network[J]. TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA,2013,23(3):788-795.
APA Yang Xiawei.,Zhu Jingchuan.,Nong Zhisheng.,He Dong.,Lai Zhonghong.,...&Liu Fawei.(2013).Prediction of mechanical properties of A357 alloy using artificial neural network.TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA,23(3),788-795.
MLA Yang Xiawei,et al."Prediction of mechanical properties of A357 alloy using artificial neural network".TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA 23.3(2013):788-795.
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