Hore, S and Das, S K and Banerjee, S and Mukherjee, S (2016) An adaptive neuro-fuzzy inference system-based modelling to predict mechanical properties of hot-rolled TRIP steel. Ironmaking & Steelmaking , 44(9) (IF-0.985). pp. 1-10.
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A model based on adaptive neural network formalism coupled with fuzzy inference system has been developed to predict mechanical properties of hot-rolled TRIP steel. The developed model incorporates a wide range of data containing chemical compositions, thermo-mechanical processing parameters and mechanical properties of hot-rolled TRIP steel. A compact set of process variables has been selected as the model inputs for predicting tensile strength, yield strength, elongation and retained austenite under a given operating condition. The model predictions show that carbon, silicon and manganese content have a significant effect on the retained austenite which increases with the increased amount of these elements. The microalloying elements such as niobium and molybdenum have a little effect on the volume fraction of retained austenite. The present model provides a predictive platform for possible application of these artificial intelligence-based tools for automation, real-time process control and operator guidance in plant operation. An adaptive neuro-fuzzy inference system-based modelling to predict mechanical properties of hot-rolled TRIP steel. Available from: https://www.researchgate.net/publication/308386727_An_adaptive_neuro-fuzzy_inference_system-based_modelling_to_predict_mechanical_properties_of_hot-rolled_TRIP_steel [accessed Aug 14, 2017].
|Uncontrolled Keywords:||Thermo-mechanical processing, TRIP steel, Mechanical properties, Neuro-fuzzy model, Coiling temperature, Retained austenite|
|Divisions:||Material Science and Technology|
|Deposited By:||Sahu A K|
|Deposited On:||14 Aug 2017 13:40|
|Last Modified:||15 Nov 2017 15:34|
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