Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion

Authors:
Sumantra Mandal;P. V. Sivaprasad;S. Venugopal;K. P. N. Murthy
Affiliations:
Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu, India;Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu, India;Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamil Nadu, India;School of Physics, University of Hyderabad, Hyderabad 500046, Andhra Pradesh, India
Venue:
Applied Soft Computing
Year:
2009

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Abstract

The deformation behavior of type 304L stainless steel during hot torsion is investigated using artificial neural network (ANN). Torsion tests in the temperature range of 600-1200^oC and in the (maximum surface) strain rate range of 0.1-100s^-^1 were carried out. These experiments provided the required data for training the neural network and for subsequent testing. The input parameters of the model are strain, log strain rate and temperature while torsional flow stress is the output. A three layer feed-forward network was trained with standard back propagation (BP) and Resilient propagation (Rprop) algorithm. The paper makes a robust comparison of the performances of the above two algorithms. The network trained with Rprop algorithm is found to perform better and also needs less number of iterations for convergence. The developed ANN model employing this algorithm could efficiently track the work hardening, dynamic softening and flow localization regions of the deforming material. Sensitivity analysis showed that temperature and strain rate are the most significant parameters while strain affects the flow stress only moderately. The ANN model, described in this paper, is an efficient quantitative tool to evaluate and predict the deformation behavior of type 304L stainless steel during hot torsion.