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: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6877efa5a65d4c2f999a2270a37b7b39 |
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| Sumario: | 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. |
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