Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks

To study the long-term creep behavior prediction of polymethacrylimide (PMI) foams, the gradation loading creep tests were proposed at four different temperatures in this paper. The Stepped Isostress (SSM) and TTSP methods were combined to obtain the master curve under reference stress and temperatu...

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Autores principales:	Jianlin Zhong, Chunhao Yang, Wuning Ma, Zhendong Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Creep Polymethacrylimide form Stepped isostress method Artificial neural networks Polymers and polymer manufacture TP1080-1185
Acceso en línea:	https://doaj.org/article/d6c0973112344a44bad2ec718bce3e07
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id	oai:doaj.org-article:d6c0973112344a44bad2ec718bce3e07
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spelling	oai:doaj.org-article:d6c0973112344a44bad2ec718bce3e072021-11-24T04:23:56ZLong-term creep behavior prediction of polymethacrylimide foams using artificial neural networks0142-941810.1016/j.polymertesting.2020.106893https://doaj.org/article/d6c0973112344a44bad2ec718bce3e072021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S014294182032122Xhttps://doaj.org/toc/0142-9418To study the long-term creep behavior prediction of polymethacrylimide (PMI) foams, the gradation loading creep tests were proposed at four different temperatures in this paper. The Stepped Isostress (SSM) and TTSP methods were combined to obtain the master curve under reference stress and temperature. The artificial Neural Networks (ANN) technique was used to build the long-term creep behavior prediction model of PMI materials. The effects of different activation functions, hidden layer structures, and other super parameters on the prediction performance were investigated. The results suggest that the SSM plus TTSP method can be used to construct the master curve, which could predict a larger time scale of material creep behavior based on a short-term test. It is of great significance and feasible to predicts the long-term creep life of materials accurately using advanced artificial intelligence technology. According to the statistical analysis, the logistic type activation function has a more accurate and stable prediction performance on long-term creep behavior prediction of PMI. To avoid overfitting, the number of hidden layers should be as small as possible, and the prediction performance of a single hidden layer structure with 8 neurons is sufficient for long-term creep behavior prediction in the engineering area. The statistical value of the correlation coefficient was greater than 0.995. The application range of advanced artificial intelligence technology in this field can be further expanded in the preceding research, such as in the prediction of long-term creep behavior on the composition level of the material.Jianlin ZhongChunhao YangWuning MaZhendong ZhangElsevierarticleCreepPolymethacrylimide formStepped isostress methodArtificial neural networksPolymers and polymer manufactureTP1080-1185ENPolymer Testing, Vol 93, Iss , Pp 106893- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Creep Polymethacrylimide form Stepped isostress method Artificial neural networks Polymers and polymer manufacture TP1080-1185
spellingShingle	Creep Polymethacrylimide form Stepped isostress method Artificial neural networks Polymers and polymer manufacture TP1080-1185 Jianlin Zhong Chunhao Yang Wuning Ma Zhendong Zhang Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
description	To study the long-term creep behavior prediction of polymethacrylimide (PMI) foams, the gradation loading creep tests were proposed at four different temperatures in this paper. The Stepped Isostress (SSM) and TTSP methods were combined to obtain the master curve under reference stress and temperature. The artificial Neural Networks (ANN) technique was used to build the long-term creep behavior prediction model of PMI materials. The effects of different activation functions, hidden layer structures, and other super parameters on the prediction performance were investigated. The results suggest that the SSM plus TTSP method can be used to construct the master curve, which could predict a larger time scale of material creep behavior based on a short-term test. It is of great significance and feasible to predicts the long-term creep life of materials accurately using advanced artificial intelligence technology. According to the statistical analysis, the logistic type activation function has a more accurate and stable prediction performance on long-term creep behavior prediction of PMI. To avoid overfitting, the number of hidden layers should be as small as possible, and the prediction performance of a single hidden layer structure with 8 neurons is sufficient for long-term creep behavior prediction in the engineering area. The statistical value of the correlation coefficient was greater than 0.995. The application range of advanced artificial intelligence technology in this field can be further expanded in the preceding research, such as in the prediction of long-term creep behavior on the composition level of the material.
format	article
author	Jianlin Zhong Chunhao Yang Wuning Ma Zhendong Zhang
author_facet	Jianlin Zhong Chunhao Yang Wuning Ma Zhendong Zhang
author_sort	Jianlin Zhong
title	Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
title_short	Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
title_full	Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
title_fullStr	Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
title_full_unstemmed	Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
title_sort	long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d6c0973112344a44bad2ec718bce3e07
work_keys_str_mv	AT jianlinzhong longtermcreepbehaviorpredictionofpolymethacrylimidefoamsusingartificialneuralnetworks AT chunhaoyang longtermcreepbehaviorpredictionofpolymethacrylimidefoamsusingartificialneuralnetworks AT wuningma longtermcreepbehaviorpredictionofpolymethacrylimidefoamsusingartificialneuralnetworks AT zhendongzhang longtermcreepbehaviorpredictionofpolymethacrylimidefoamsusingartificialneuralnetworks
_version_	1718416063176114176

Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks

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