Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network

IMR OpenIR

	Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network
	Tian, Wenliang; Meng, Fandi; Liu, Li; Li, Ying; Wang, Fuhui; Liu, L (reprint author), Chinese Acad Sci, Inst Met Res, Wencui Rd 62, Shenyang 110016, Peoples R China.
	2017-01-17
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	7 页码:-
摘要	A concept for prediction of organic coatings, based on the alternating hydrostatic pressure (AHP) accelerated tests, has been presented. An AHP accelerated test with different pressure values has been employed to evaluate coating degradation. And a back-propagation artificial neural network (BP-ANN) has been established to predict the service property and the service lifetime of coatings. The pressure value (P), immersion time (t) and service property (impedance modulus vertical bar Z vertical bar) are utilized as the parameters of the network. The average accuracies of the predicted service property and immersion time by the established network are 98.6% and 84.8%, respectively. The combination of accelerated test and prediction method by BP-ANN is promising to evaluate and predict coating property used in deep sea.; A concept for prediction of organic coatings, based on the alternating hydrostatic pressure (AHP) accelerated tests, has been presented. An AHP accelerated test with different pressure values has been employed to evaluate coating degradation. And a back-propagation artificial neural network (BP-ANN) has been established to predict the service property and the service lifetime of coatings. The pressure value (P), immersion time (t) and service property (impedance modulus vertical bar Z vertical bar) are utilized as the parameters of the network. The average accuracies of the predicted service property and immersion time by the established network are 98.6% and 84.8%, respectively. The combination of accelerated test and prediction method by BP-ANN is promising to evaluate and predict coating property used in deep sea.
部门归属	[tian, wenliang ; meng, fandi ; liu, li ; li, ying ; wang, fuhui] chinese acad sci, inst met res, wencui rd 62, shenyang 110016, peoples r china
学科领域	Multidisciplinary Sciences
资助者	National Natural Science Fund of China [51271187, 51622106]; National Basic Research Program of China [2014CB643303, 2014CB643301]
收录类别	SCI
语种	英语
WOS记录号	WOS:000391935900001
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/78336
专题	中国科学院金属研究所
通讯作者	Liu, L (reprint author), Chinese Acad Sci, Inst Met Res, Wencui Rd 62, Shenyang 110016, Peoples R China.
推荐引用方式 GB/T 7714	Tian, Wenliang,Meng, Fandi,Liu, Li,et al. Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network[J]. SCIENTIFIC REPORTS,2017,7:-.
APA	Tian, Wenliang,Meng, Fandi,Liu, Li,Li, Ying,Wang, Fuhui,&Liu, L .(2017).Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network.SCIENTIFIC REPORTS,7,-.
MLA	Tian, Wenliang,et al."Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network".SCIENTIFIC REPORTS 7(2017):-.