Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

The high-temperature deformation behavior of metals and alloys undergoes complex mechanisms depending on the deformation conditions. The microstructure and mechanical properties after deformation are important factors that determine the strength and durability of the final product. Therefore, many s...

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Bibliographic Details
Published inMetals (Basel ) Vol. 12; no. 2; p. 223
Main Authors Moon, In Yong, Jeong, Hi Won, Lee, Ho Won, Kim, Se-Jong, Oh, Young-Seok, Jung, Jaimyun, Oh, Sehyeok, Kang, Seong-Hoon
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2022
Subjects
Accuracy
Artificial intelligence
artificial neural network
Artificial neural networks
Constitutive equations
Constitutive relationships
Coordination compounds
Deep learning
Deformation
flow stress
High temperature
hot deformation
machine learning
Mathematical analysis
Mechanical properties
Metals
Microstructure
Neural networks
Nickel alloys
Nickel base alloys
Stress
Temperature
Yield strength
Zener–Hollomon parameter
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Summary:The high-temperature deformation behavior of metals and alloys undergoes complex mechanisms depending on the deformation conditions. The microstructure and mechanical properties after deformation are important factors that determine the strength and durability of the final product. Therefore, many studies to predict the microstructure and mechanical properties have been conducted. In this regard, numerous mathematical approaches for predicting microstructure and flow stress have been proposed over the past half century. Accordingly, many advances have been made in the field of material science. Nevertheless, there are limitations in the mathematical modeling method as there is a complex relationship between the deformation conditions and the mechanical properties. Therefore, in this study, flow stress prediction was performed by applying conventional constitutive equation and artificial intelligence technology, which is known to be effective in modeling complex relationships. As a result, it was confirmed that the flow stresses modeled by the artificial neural network showed a higher accuracy than the flow stresses modeled by the conventional Arrhenius hyperbolic sine equation.
ISSN:2075-4701
2075-4701
DOI:10.3390/met12020223