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Flow Stress Modeling of Tube and Slab Route Sheets of Zircaloy-4 Using Machine Learning Techniques and Arrhenius Type Constitutive Equations

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Abstract

The present work examines the effectiveness of a strain-compensated Arrhenius-type constitutive model, Artificial Neural Network (ANN) and Support Vector Regression (SVR), to predict the flow stress in tube route and slab route Zircaloy-4 sheets at elevated temperatures. Isothermal tensile tests were conducted from a temperature range of 298-498 K at three different strain rates, namely 0.001, 0.005, and 0.01 s−1. The predictive performance of all three models was evaluated using correlation coefficient and average absolute relative error. The results revealed that the ANN and SVR models provide more precise predictions compared to the strain-compensated Arrhenius-type constitutive model. The SVR model with a medium Gaussian kernel performed better in flow stress prediction than the quadratic kernel for tube route and slab route sheets. Among ANN and SVR, the ANN model has a better correlation coefficient, and SVR model has a low average absolute relative error. Finally, the ANN and SVR model were cross-validated and showed superior performance in predicting flow stress.

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Authors and Affiliations

  1. MLR Institute of Technology, Hyderabad, 500043, India

    Limbadri Kanthi

  2. Birla Institute of Technology & Science, Pilani, Hyderabad Campus, Secunderabad, 500078, India

    Pankaj Wankhede, Suresh Kurra & Sabareesh Geetha Rajashekharan

  3. Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, 500090, India

    Swadesh Kumar Singh

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  1. Limbadri Kanthi
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  5. Sabareesh Geetha Rajashekharan
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Correspondence to Pankaj Wankhede.

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Kanthi, L., Wankhede, P., Kurra, S. et al. Flow Stress Modeling of Tube and Slab Route Sheets of Zircaloy-4 Using Machine Learning Techniques and Arrhenius Type Constitutive Equations. J. of Materi Eng and Perform 32, 462–474 (2023). https://doi.org/10.1007/s11665-022-07102-x

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