Journal
IRONMAKING & STEELMAKING
Volume 40, Issue 4, Pages 298-304Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1179/1743281212Y.0000000047
Keywords
Neural network model; TRIP steel; Flow stress; Mechanical properties; Hot rolling; Network optimisation algorithm
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Funding
- Council of Scientific and Industrial Research, India, under the Supra Institutional Project
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Transformation induced plasticity (TRIP) steels exhibit excellent strength and ductility and can be engineered to provide excellent formability for manufacturing complex parts. In this study, a data driven multi-input multi-output multilayer perceptron based neural network model has been developed to predict the flow stress, yield strength, ultimate tensile strength and elongation as a function of composition and thermomechanical processing parameters for strip rolling of TRIP steels. The input parameters in this generalised regression artificial neural network (ANN) model are steel chemistry, cooling rate and finish roll temperature. The network training architecture has been optimised using the Broyden-Fletcher-Goldfarb-Shanno algorithm to minimise the network training error within few training cycles. The algorithm facilitates a faster convergence of network training and testing errors. There has been an excellent agreement between the ANN model predictions and the target (measured) values for flow stress and mechanical properties depicted by the respective regression fit between these values.
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