Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results

Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results

Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (<i>K<sub>J</sub></i><sub>c</sub>) of ferritic steels in the ductile-to-brittle transition region. Consequen...

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Autores principales: Kenichi Ishihara, Hayato Kitagawa, Yoichi Takagishi, Toshiyuki Meshii
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
fracture toughness
machine learning
artificial neural network
predictor
yield stress
tensile strength
Mining engineering. Metallurgy
TN1-997
Acceso en línea:https://doaj.org/article/6877efa5a65d4c2f999a2270a37b7b39
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spelling oai:doaj.org-article:6877efa5a65d4c2f999a2270a37b7b392021-11-25T18:21:36ZApplication of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results10.3390/met111117402075-4701https://doaj.org/article/6877efa5a65d4c2f999a2270a37b7b392021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4701/11/11/1740https://doaj.org/toc/2075-4701Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (<i>K<sub>J</sub></i><sub>c</sub>) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting <i>K<sub>J</sub></i><sub>c</sub> from minimal testing has been of interest for a long time. In this study, a Windows-ready <i>K<sub>J</sub></i><sub>c</sub> predictor based on tensile properties (specifically, yield stress σ<sub>YSRT</sub> and tensile strength <i>σ</i><sub>BRT</sub> at room temperature (RT) and <i>σ</i><sub>YS</sub> at <i>K<sub>J</sub></i><sub>c</sub> prediction temperature) was developed by applying an artificial neural network (ANN) to 531 <i>K<sub>J</sub></i><sub>c</sub> data points. If the <i>σ</i><sub>YS</sub> temperature dependence can be adequately described using the Zerilli–Armstrong <i>σ</i><sub>YS</sub> master curve (MC), the necessary data for <i>K<sub>J</sub></i><sub>c</sub> prediction are reduced to <i>σ</i><sub>YSRT</sub> and <i>σ</i><sub>BRT</sub>. The developed <i>K<sub>J</sub></i><sub>c</sub> predictor successfully predicted <i>K<sub>J</sub></i><sub>c</sub> under arbitrary conditions. Compared with the existing ASTM E1921 <i>K<sub>J</sub></i><sub>c</sub> MC, the developed <i>K<sub>J</sub></i><sub>c</sub> predictor was especially effective in cases where <i>σ</i><sub>B</sub>/<i>σ</i><sub>YS</sub> of the material was larger than that of RPV steel.Kenichi IshiharaHayato KitagawaYoichi TakagishiToshiyuki MeshiiMDPI AGarticlefracture toughnessmachine learningartificial neural networkpredictoryield stresstensile strengthMining engineering. MetallurgyTN1-997ENMetals, Vol 11, Iss 1740, p 1740 (2021)
institution DOAJ
collection DOAJ
language EN
topic fracture toughness
machine learning
artificial neural network
predictor
yield stress
tensile strength
Mining engineering. Metallurgy
TN1-997
spellingShingle fracture toughness
machine learning
artificial neural network
predictor
yield stress
tensile strength
Mining engineering. Metallurgy
TN1-997
Kenichi Ishihara
Hayato Kitagawa
Yoichi Takagishi
Toshiyuki Meshii
Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results
description Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (<i>K<sub>J</sub></i><sub>c</sub>) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting <i>K<sub>J</sub></i><sub>c</sub> from minimal testing has been of interest for a long time. In this study, a Windows-ready <i>K<sub>J</sub></i><sub>c</sub> predictor based on tensile properties (specifically, yield stress σ<sub>YSRT</sub> and tensile strength <i>σ</i><sub>BRT</sub> at room temperature (RT) and <i>σ</i><sub>YS</sub> at <i>K<sub>J</sub></i><sub>c</sub> prediction temperature) was developed by applying an artificial neural network (ANN) to 531 <i>K<sub>J</sub></i><sub>c</sub> data points. If the <i>σ</i><sub>YS</sub> temperature dependence can be adequately described using the Zerilli–Armstrong <i>σ</i><sub>YS</sub> master curve (MC), the necessary data for <i>K<sub>J</sub></i><sub>c</sub> prediction are reduced to <i>σ</i><sub>YSRT</sub> and <i>σ</i><sub>BRT</sub>. The developed <i>K<sub>J</sub></i><sub>c</sub> predictor successfully predicted <i>K<sub>J</sub></i><sub>c</sub> under arbitrary conditions. Compared with the existing ASTM E1921 <i>K<sub>J</sub></i><sub>c</sub> MC, the developed <i>K<sub>J</sub></i><sub>c</sub> predictor was especially effective in cases where <i>σ</i><sub>B</sub>/<i>σ</i><sub>YS</sub> of the material was larger than that of RPV steel.
format article
author Kenichi Ishihara
Hayato Kitagawa
Yoichi Takagishi
Toshiyuki Meshii
author_facet Kenichi Ishihara
Hayato Kitagawa
Yoichi Takagishi
Toshiyuki Meshii
author_sort Kenichi Ishihara
title Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results
title_short Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results
title_full Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results
title_fullStr Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results
title_full_unstemmed Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results
title_sort application of an artificial neural network to develop fracture toughness predictor of ferritic steels based on tensile test results
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/6877efa5a65d4c2f999a2270a37b7b39
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AT yoichitakagishi applicationofanartificialneuralnetworktodevelopfracturetoughnesspredictorofferriticsteelsbasedontensiletestresults
AT toshiyukimeshii applicationofanartificialneuralnetworktodevelopfracturetoughnesspredictorofferriticsteelsbasedontensiletestresults
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