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Abstract Metals and alloys are widely used in industry due to their excellent mechanical properties, and researchers are committed to finding new materials with better properties or mechanisms to enhance properties of materials. In the forming process of metal and alloy materials, the hot deformation can refine the grain effectively to improve the mechanical properties such as yield strength and tensile strength. Therefore, it is necessary to study the deformation behavior of metal and alloy materials at high temperature. Hyperbolic–sinusoidal Arrhenius-type model is widely used by researchers because of its good simulation effect at high temperatures. This paper studies the building process of the model and optimizes the modeling process with the help of neural network model. A neural network model is constructed to efficiently determine the hyperbolic–sinusoidal Arrhenius-type equations, based on which the flow stress of high-entropy alloys (HEAs) for different high temperatures and strain rates can be well predicted. In this study, the reported hot deformation behaviors of Al0.3CoCrFeNi HEAs are examined by current model. The results show that the coefficients obtained by the neural network method can better describe the experimental hot flow stress, especially at high strain rate or low temperature conditions. The root mean square error (RMSE) and the correlation coefficient(R) are used to assess the degree of difference between the results. The RMSE and R of the neural network method at total data are 27.7 and 0.985, respectively, which are better than 33.1 and 0.979 of the traditional method. To show the general applicability of the model, the hot deformation behaviors of (CoCrNi)94Ti3Al3, FeCrCuNi2Mn2 and AlCrCuFeNi are analyzed by the model. The research work in this paper can improve the efficiency and accuracy of hyperbolic–sinusoidal Arrhenius-type model, reduce the difficulty of establishing the model, and has positive significance for the wide use of the model.
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Received: 01 August 2023
Published: 04 June 2024
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Corresponding Authors:
Miaolin Feng
E-mail: mlfeng@sjtu.edu.cn
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2024, Vol. 45 
