N Subba Reddy | Gyeongsang National University (original) (raw)

Papers by N Subba Reddy

Neural networks, which are known for mapping non-linear and complex systems, have been used in th... more Neural networks, which are known for mapping non-linear and complex systems, have been used in the present study to model the grainrefinement behavior of Al-7Si alloy. The development of a feed forward neural network (FFNN) model with back-propagation (BP) learning algorithm has been presented for the prediction of the grain size, as a function of Ti and B addition level and holding time during grain refinement of Al-7Si alloy. Comparison of the predicted and experimental results shows that the FFNN model can predict the grain size of Al-7Si alloy with good learning precision and generalization.

This investigation was aimed to study the effect of nickel addition on the sintering behaviour of... more This investigation was aimed to study the effect of nickel addition on the sintering behaviour of Ti 3 SiC 2 powder under pressureless conditions. Nearly pure bulk Ti 3 SiC 2 ceramic with relative density of ∼98.5% was produced at 1500 • C by sintering of Ti 3 SiC 2 powder while using 1 wt.% nickel as a sintering aid. The activation energy of sintering of Ti 3 SiC 2 powder was determined to be 351 ± 5 kJ/mol, which was decreased slightly to 305 ± 10 kJ/mol when nickel (1 wt.%) was added. Sintering of Ti 3 SiC 2 powder was found to be controlled by mixed mode of mechanisms, i.e., the interface reactions and diffusion of Si atoms. The mechanism was changed to liquid phase sintering due to melting of Ni-based compounds in the sample sintered with Ni. The reaction of Ni with Ti 3 SiC 2 helped to decrease the grain growth rate. The hardness (Vickers), flexural strength and fracture toughness of the sintered Ti 3 SiC 2 -1Ni sample were found to be 3.4 GPa, 311 ± 22 MPa and 2.8-6.4 MPa m 1/2 , respectively.

Two different approaches were applied to describe flow softening during high-temperature deformat... more Two different approaches were applied to describe flow softening during high-temperature deformation of Ti-6Al-4V with a lamellar microstructure. One was proposed on the basis of morphological evolution from lamellar to globular alpha particles while the other was suggested in the context of colony rotation and alpha particle coarsening. The former was not capable of predicting the flow behavior, implying that flow softening was less related to dynamic spheroidization. In contrast, the latter provided an accurate prediction of the flow response of the alloy with different alpha platelet thicknesses of 0.4-8.0 lm over typical processing temperatures (815-950°C) and strain rates (0.1 and 1.0 s À1 ).

An artificial neural network (ANN) model has been developed for the analysis and simulation of th... more An artificial neural network (ANN) model has been developed for the analysis and simulation of the correlation between the mechanical properties and composition and heat treatment parameters of low alloy steels. The input parameters of the model consist of alloy compositions (C, Si, Mn, S, P, Ni, Cr, Mo, Ti, and Ni) and heat treatment parameters (cooling rate and tempering temperature). The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, percentage elongation, reduction in area and impact energy. The model can be used to calculate the properties of low alloy steels as a function of alloy composition and heat treatment variables. The individual and the combined influence of inputs on properties of medium carbon steels is simulated using the model. The current study achieved a good performance of the ANN model, and the results are in agreement with experimental knowledge. Explanation of the calculated results from the metallurgical point of view is attempted. The developed model can be used as a guide for further alloy development. (N.S. Reddy). degree of error. Similarly, it is not easy to use statistical methods to relate multiple inputs to multiple process outputs. The method using neural networks (NN), on the other hand, has been identified as a suitable way for overcoming these difficulties. NN are mathematical models and algorithms that imitate certain aspects of the information-processing and knowledge-gathering methods of the human nervous system . A NN can perform highly complex mappings on nonlinearly related data by inferring subtle relationships between input and output parameters . It can, in principle, generalize from a limited quantity of training data to overall trends in functional relationships . Although several network architectures and training algorithms are available, the feed-forward neural network with the back-propagation (BP) learning algorithm is more commonly used . Therefore, within the last decade, the application of neural networks in the materials science research has steadily increased. A number of reviews carried out recently have identified the application of neural networks to a diverse range of materials science applications . The objective of the present work is, therefore, to develop a neural network model, which can predict the properties for a given composition and heat treatment, and the relationship of the properties with respect to these input variables.

Neural networks, which are known for mapping non-linear and complex systems, have been used in th... more Neural networks, which are known for mapping non-linear and complex systems, have been used in the present study to model the grainrefinement behavior of Al-7Si alloy. The development of a feed forward neural network (FFNN) model with back-propagation (BP) learning algorithm has been presented for the prediction of the grain size, as a function of Ti and B addition level and holding time during grain refinement of Al-7Si alloy. Comparison of the predicted and experimental results shows that the FFNN model can predict the grain size of Al-7Si alloy with good learning precision and generalization.

This investigation was aimed to study the effect of nickel addition on the sintering behaviour of... more This investigation was aimed to study the effect of nickel addition on the sintering behaviour of Ti 3 SiC 2 powder under pressureless conditions. Nearly pure bulk Ti 3 SiC 2 ceramic with relative density of ∼98.5% was produced at 1500 • C by sintering of Ti 3 SiC 2 powder while using 1 wt.% nickel as a sintering aid. The activation energy of sintering of Ti 3 SiC 2 powder was determined to be 351 ± 5 kJ/mol, which was decreased slightly to 305 ± 10 kJ/mol when nickel (1 wt.%) was added. Sintering of Ti 3 SiC 2 powder was found to be controlled by mixed mode of mechanisms, i.e., the interface reactions and diffusion of Si atoms. The mechanism was changed to liquid phase sintering due to melting of Ni-based compounds in the sample sintered with Ni. The reaction of Ni with Ti 3 SiC 2 helped to decrease the grain growth rate. The hardness (Vickers), flexural strength and fracture toughness of the sintered Ti 3 SiC 2 -1Ni sample were found to be 3.4 GPa, 311 ± 22 MPa and 2.8-6.4 MPa m 1/2 , respectively.

Two different approaches were applied to describe flow softening during high-temperature deformat... more Two different approaches were applied to describe flow softening during high-temperature deformation of Ti-6Al-4V with a lamellar microstructure. One was proposed on the basis of morphological evolution from lamellar to globular alpha particles while the other was suggested in the context of colony rotation and alpha particle coarsening. The former was not capable of predicting the flow behavior, implying that flow softening was less related to dynamic spheroidization. In contrast, the latter provided an accurate prediction of the flow response of the alloy with different alpha platelet thicknesses of 0.4-8.0 lm over typical processing temperatures (815-950°C) and strain rates (0.1 and 1.0 s À1 ).

An artificial neural network (ANN) model has been developed for the analysis and simulation of th... more An artificial neural network (ANN) model has been developed for the analysis and simulation of the correlation between the mechanical properties and composition and heat treatment parameters of low alloy steels. The input parameters of the model consist of alloy compositions (C, Si, Mn, S, P, Ni, Cr, Mo, Ti, and Ni) and heat treatment parameters (cooling rate and tempering temperature). The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, percentage elongation, reduction in area and impact energy. The model can be used to calculate the properties of low alloy steels as a function of alloy composition and heat treatment variables. The individual and the combined influence of inputs on properties of medium carbon steels is simulated using the model. The current study achieved a good performance of the ANN model, and the results are in agreement with experimental knowledge. Explanation of the calculated results from the metallurgical point of view is attempted. The developed model can be used as a guide for further alloy development. (N.S. Reddy). degree of error. Similarly, it is not easy to use statistical methods to relate multiple inputs to multiple process outputs. The method using neural networks (NN), on the other hand, has been identified as a suitable way for overcoming these difficulties. NN are mathematical models and algorithms that imitate certain aspects of the information-processing and knowledge-gathering methods of the human nervous system . A NN can perform highly complex mappings on nonlinearly related data by inferring subtle relationships between input and output parameters . It can, in principle, generalize from a limited quantity of training data to overall trends in functional relationships . Although several network architectures and training algorithms are available, the feed-forward neural network with the back-propagation (BP) learning algorithm is more commonly used . Therefore, within the last decade, the application of neural networks in the materials science research has steadily increased. A number of reviews carried out recently have identified the application of neural networks to a diverse range of materials science applications . The objective of the present work is, therefore, to develop a neural network model, which can predict the properties for a given composition and heat treatment, and the relationship of the properties with respect to these input variables.