Journal Published Online: 04 June 2020
Volume 9, Issue 2

Study on Hot Deformation Behavior of High Carbon Low Alloy Steel by Constitutive and ANN Modeling and Development of Processing Maps

CODEN: MPCACD

Abstract

The hot deformation behavior of high carbon low alloy steel was investigated by isothermal compression tests on Gleeble 3800 thermomechanical simulator in the temperature range of 850°C–1,100°C and in the strain rate range of 0.01 s−1 to 10 s−1. The Arrhenius-type constitutive equations model and the artificial neural network (ANN) model were developed to investigate and predict the flow behavior of steel. The result of constitutive modeling showed that the material constants can be represented as a sixth degree polynomial function. The average apparent activation energy for hot deformation decreased from 441 kJ/mol to 350 kJ/mol, and the stress exponent decreased from 5.6 to 4.01, indicating the deformation mechanism to be dislocation glide and climb. The accuracy and effectiveness of the two models were compared using correlation coefficient and mean absolute error. Results showed that the ANN model almost perfectly predicted the flow behavior in terms of flow stress. Mean absolute errors in the Arrhenius-type constitutive equations model and ANN model are 7.2 % and 2.1 %, respectively. This confirms the better predictability of the ANN model over the analytical constitutive equations–based model. Processing maps were constructed using the dynamic material model, modified dynamic material model, and strain rate sensitivity maps at true strains of 0.3 and 0.6, which were correlated with microstructure evolution. It was observed that the instability region shrinks with increasing strain, which can be attributed to the fact that, for longer deformation, the stress value becomes constant and hardening–softening cycles cease to exist. Metallographic investigations showed the variety of instabilities such as cracks, voids, pores, and adiabatic shear bands, confirming the unstable region predicted by the processing maps.

Author Information

Kumar, Deepak
Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India
Kumar, Sumit
Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India
Nath, S. K.
Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India
Pages: 17
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Stock #: MPC20190036
ISSN: 2379-1365
DOI: 10.1520/MPC20190036