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Development of constitutive models for dynamic strain aging regime in Austenitic stainless steel 304
Highlights ► Tensile testing of Austenitic stainless steel 304 at elevated temperatures. ► Prediction of flow stress in Dynamic Strain Aging (DSA) regime. ► Constitutive modeling using JC, modified ZA, modified Arrhenius type, and ANN models.
Abstract The experimental stress–strain data from isothermal tensile tests over a wide range of temperatures (623–923K at an interval of 50K), strains (0.02–0.30 at an interval of 0.02) and strain rates (0.0001, 0.001, 0.01, 0.1s−1) were employed to determine the Dynamic Strain Aging (DSA) regime and to formulate a suitable constitutive model to predict the elevated-temperature deformation behavior in DSA regime of Austenitic Stainless Steel (ASS) 304. Four models, namely, Johnson Cook (JC) model, modified Zerilli–Armstrong (m-ZA) model, modified Arrhenius type equations (m-Arr) and Artificial Neural Networks (ANNs), were investigated. Suitability of these models was evaluated by comparing the correlation coefficient, average absolute error and its standard deviation. It was observed that JC, m-ZA and m-Arr model could not effectively predict flow stress behavior of ASS304 in DSA regime, while the predictions by ANN model are found to be in good agreement with the experimental data.
Development of constitutive models for dynamic strain aging regime in Austenitic stainless steel 304
Highlights ► Tensile testing of Austenitic stainless steel 304 at elevated temperatures. ► Prediction of flow stress in Dynamic Strain Aging (DSA) regime. ► Constitutive modeling using JC, modified ZA, modified Arrhenius type, and ANN models.
Abstract The experimental stress–strain data from isothermal tensile tests over a wide range of temperatures (623–923K at an interval of 50K), strains (0.02–0.30 at an interval of 0.02) and strain rates (0.0001, 0.001, 0.01, 0.1s−1) were employed to determine the Dynamic Strain Aging (DSA) regime and to formulate a suitable constitutive model to predict the elevated-temperature deformation behavior in DSA regime of Austenitic Stainless Steel (ASS) 304. Four models, namely, Johnson Cook (JC) model, modified Zerilli–Armstrong (m-ZA) model, modified Arrhenius type equations (m-Arr) and Artificial Neural Networks (ANNs), were investigated. Suitability of these models was evaluated by comparing the correlation coefficient, average absolute error and its standard deviation. It was observed that JC, m-ZA and m-Arr model could not effectively predict flow stress behavior of ASS304 in DSA regime, while the predictions by ANN model are found to be in good agreement with the experimental data.
Development of constitutive models for dynamic strain aging regime in Austenitic stainless steel 304
Gupta, Amit Kumar (author) / Krishnamurthy, Hansoge Nitin (author) / Singh, Yashjeet (author) / Prasad, Kaushik Manga (author) / Singh, Swadesh Kumar (author)
2012-09-23
12 pages
Article (Journal)
Electronic Resource
English
Development of constitutive models for dynamic strain aging regime in Austenitic stainless steel 304
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