Abstract
The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ∘ C with an interval of 100 ∘ C and strain rates ranging from 0.05 to 1.0 s − 1 . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( R 2 ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.
Highlights
Medium carbon steel materials are generally employed for a wide range of engineering applications due to their vital mechanical properties such as wear resistance and weldability [1].It is obvious that most of the automobile components are manufactured by causing the plastic deformation in the work material and the manufactured components experience the plastic deformation in real time applications
To describe the material behavior, several number of mathematical models are proposed, but the available flow stress models such as Johnson–Cook (JC), modified Johnson–Cook (MJC), modified Zerilli–Armstrong (MZA), and Arrhenius-type constitutive (AC) models displayed a low accuracy on the flow stress prediction in some tested materials [5,6,7,8,9]
The experimental procedures used in this present work to characterize medium carbon steel material behavior as follows: at first, the specimens were produced from the water-jet cutting process and three samples were used for each test condition
Summary
Medium carbon steel materials are generally employed for a wide range of engineering applications due to their vital mechanical properties such as wear resistance and weldability [1]. Li et al [13] did the isothermal uniaxial tensile tests under elevated temperatures (20–900 ◦ C) and high strain rates (0.01–10.0 s−1 ) for four kinds of boron steel B1500HS material and investigated the problem using modified AC and MJC models They concluded that from comparison with a computational data, the constitutive equations provided better correlations against the experimental measurements. Conducted constitutive analysis to describe high temperature flow behavior of 3Cr–1Si–1Ni ultra-high strength steel and proposed an AC model that accounted strain compensation showed much capability in describing the material behavior at hot working conditions He et al [16] verified existing flow stress models capability to capture the flow behavior in Ti2AlNb-based alloys and the constitutive equation displayed a more precise description against the experimental data. ANN-BP and ANN-BP/OP models are tested against experimental measurements and constructed model verification’s are discussed using both graphical and numerical validations
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