Prediction of flow stress for carbon steels using recurrent self-organizing neuro fuzzy networks

TLDR: A hybrid neural network model better known as RSONFN (Recurrent Self-Organizing Neural Network Model) is applied to predict the flow stress for carbon steels to prove its superiority over other existing tools.

Abstract: Mechanical properties of any material are extensively influenced by the parameters such as strain, strain rate, temperature, and its composition. The characteristics of any material such as ductility, strain hardening, strength, dynamic recovery, grain growth, and recrystallization are greatly affected by the influence of various process parameters. So, it is essential to have the knowledge of the constitutive relationships that relate different process variables to flow stress of the deforming material which estimates various parameters such as load, energy, and stress in the metal forming operations. A consistent effort has been gone into developing the constitutive equations for the detailed mathematical description of the flow curves and the aforementioned parameters for years now. Soft computing tools that concern computation of an imprecise environment and model very complex systems those are based on input-output relationship have gained significant attention in recent years. The intricacies of the mathematical modeling of the mechanical properties of the material, enticed the artificial research community to take this as a challenge. One such soft computing tool neural network is applied in this field to predict the behavior accurately. In this paper, a hybrid neural network model better known as RSONFN (Recurrent Self-Organizing Neural Network Model) is applied to predict the flow stress for carbon steels. The RSONFN is having the advantages of the well-established technologies of the artificial intelligence tools such as Fuzzy logic to capture long range data sets and neural networks. The RSONFN structure is a dynamic one as the numbers of its layers as well as the numbers of nodes in each layer of the network are not predetermined. Such an attribute differentiate it from the Multilayer perceptron which is having static structure. The results obtained by this network prove its superiority over other existing tools.