Subhas Ganguly | NIT RAIPUR (original) (raw)

Papers by Subhas Ganguly

Research paper thumbnail of Magneto-dielectric properties of B-site doped (Fe3+, Zr4+) BiMnO3 perovskite ceramic

Materials Science and Engineering: B

Research paper thumbnail of A study on residual stress generation versus recrystallisation in FSW joint of dissimilar alloy pair of the AA5083 and HSLA steel

Science and Technology of Welding and Joining

Research paper thumbnail of Distribution of intermetallic compounds in dissimilar joint interface of AA 5083 and HSLA steel welded by FSW technique

Research paper thumbnail of Evolution of Residual Stresses in Friction Stir Welded Joints of AA7039

Journal of The Institution of Engineers (India): Series D

Research paper thumbnail of ISIJ International, Vol. 47 (2007), No. 8, pp. 1195–1203

High strength multiphase steel is the one of the commonly used steels due to its reasonable combi... more High strength multiphase steel is the one of the commonly used steels due to its reasonable combination of strength and ductility. The maximization of strength without much compromise to the ductility is an important objective in designing the alloy along with its processing parameters. The strength of low carbon steel is improved by the addition of alloying and microalloying elements including carbide formers and other precipitation hardeners, and also by modifying the microstructure through introduction of the low temperature transformation products of austenite, e.g., bainite and martensite, which generally leads to deterioration in ductility. The design of steels from fundamental principles is a rather complex task. The determination of mechanical properties of steels in terms of their composition and process parameters requires adequate models, which can suggest a tailored change in the variables to achieve a certain target property. Thermomechanical processing is known to impr...

Research paper thumbnail of New training strategies for neural networks with application to quaternary Al–Mg–Sc–Cr alloy design problems

Applied Soft Computing, 2016

Display Omitted The training of a neural network in multiple stages.A dual stage multi-resource d... more Display Omitted The training of a neural network in multiple stages.A dual stage multi-resource data training scheme using multi-objective genetic algorithm.Development of efficient neural network model focusing on missing, but most informative domains of the dataset.The scheme is used for Al-Mg-Cr-Sc alloy system. This study concerns the training of a neural network in multiple stages considering minimization of errors from multiple data/pattern resources. The paper proposed a dual stage multi-resource data training scheme using multi-objective genetic algorithm. The training scheme has been used for the design and development of efficient neural network model focusing on missing, but most informative domains of the data set by means of introducing only a few patterns from missing domain treated separately during the later stage of training. The trained model has been used to design a quaternary Al-Mg-Cr-Sc alloy system, from the information subsets of binary Al-Cr and the ternary Al-Mg-Sc alloys. The validity of the proposed algorithm has been discussed in light of the evolution of the ageing characteristics of the new aluminium alloy system.

Research paper thumbnail of Influence of frictional heat spread pattern on the formation of intermetallic layers at the dissimilar FSW joint interface between AA 5083 and HSLA steel

Journal of Manufacturing Processes

Research paper thumbnail of Synthesis of novel nanostructured 0.6BMO-0.4BT perovskite ceramic and its thermal, structural and mechanical characteristics

Materials Today: Proceedings

Research paper thumbnail of Investigation of the thermal properties of Cu–Ag core-shell nanowires using molecular dynamics simulation

Physica B: Condensed Matter

Research paper thumbnail of Hierarchical Oxide Nanostructures-Based Gas Sensor: Recent Advances

Materials Horizons: From Nature to Nanomaterials, 2020

Chemoresistive sensors based on metal oxide semiconductor (MOS) materials have been extensively i... more Chemoresistive sensors based on metal oxide semiconductor (MOS) materials have been extensively investigated for the sake of identifying various toxic, hazardous, and explosive gases owing to their exceptional benefits, for example, such as outstanding sensitivity, cost-effectiveness, ease of fabrication, and facile integration. Over the years, it has been understood that factors that signifies sensing properties such as gas response, selectivity, stability, and promptness to response/recovery relies upon materials and morphology sensing materials, transducer designs, and few other factors. However, sluggish response, selectivity issues in real environment, higher operational temperature, and thermomechanical stability limits its widespread applications in the field of gases as well as organic vapor monitoring. In view of the above challenges, severe efforts have been made by scientific community to offset the as-described deficiencies by multiple strategies. In this aspect, oxide-based hierarchical nanostructures display strong interactions with the reacting species, eventually yielding outstanding sensing properties in comparison to other simple nano- and/or microstructures. A vast range of diverse morphologies and nano-, micro-, or mesoscale structures have been studied during recent past, each one revealing promising sensing properties toward specific chemical compounds. This chapter summarizes a comprehensive database based on previous notable works as well as latest developments in the synthesis, fabrication, and characterization of hierarchical metal oxide-based gas sensors for multiple applications.

Research paper thumbnail of Sintering effect on the structure and multiferroic behavior of nanostructured BiMnO3 ceramic synthesized by mechanochemical route

Ferroelectrics, 2021

Abstract BiMnO3 (BMO) ceramics was synthesized and sintered by mechanochemical method. Effect of ... more Abstract BiMnO3 (BMO) ceramics was synthesized and sintered by mechanochemical method. Effect of sintering on the structure and multiferroic was investigated by XRD, FESEM, HRTEM, ferroelectric and magnetic study. These studies synchronously exhibited the formation of distinct nanostructured monoclinic perovskite (C2 space group) phase with crystallite size range from 5 to 40 nm. The dielectric study revealed a significant dependence of εr and tanδ with the change in sintering temperature. P-E loop and magnetization studies revealed a trend of increase in Pr (0.396 μC/cm2 to 0.50 μC/cm2) and decrease in Ms (0.125 to 0.085) respectively with the rise in sintering temperature.

Research paper thumbnail of Effect of oxygen vacancies on the dielectricity of Ga doped equimolar BiMnO3–BaTiO3 characterized by XPS analysis

Physica B: Condensed Matter, 2021

Research paper thumbnail of Development of a blast-induced vibration prediction model using an artificial neural network

Journal of the Southern African Institute of Mining and Metallurgy, 2019

In an opencast coal mine explosives are used for fragmentation of coal and overburden. If the exp... more In an opencast coal mine explosives are used for fragmentation of coal and overburden. If the explosive energy is not fully utilized it causes blast-induced ground vibration, which may damage nearby structures. Ground vibration is expressed as peak particle velocity (PPV). During different stages of mine planning and operation, it is necessary to use a ground vibration prediction model for blasthole design. Selection of the modelling technique is crucial. Mathematical and statistical modelling techniques have limited application because of the lack of explicit knowledge about the complex mine blasting system. Vogiatzi (2002) highlighted the problem of multicollinearity in case of statistical modeling techniques. Mutalib et al. (2013) stated that mathematical models are unable to capture the nonlinear relationship between several blasting-related parameters due to the complexity of the model input data. However, the difficulty involved in modelling complex blast vibration problems can be removed by adopting an alternative soft computing modelling approach. One of the soft computing techniques is the artificial neural network (ANN). Ragam and Nimaje (2018) developed an ANN model for predicting PPV using six input variables. Kosti et al. (2013) stated that the conventional predictors fail to provide acceptable prediction accuracy. They showed that a neural network model with four mine blast parameters as input could make significantly more accurate on-site predictions. Sayadi et al., (2013), using a database from Teheran Cement Company limestone mines, found that a neural network resulted in maximum accuracy and minimum error. Khandelwal and Singh (2009) developed an ANN model using 150 data records from an Indian coal mine with site-specific rock characteristics and geomining setting. Khandewal and Singh (2007) built a ground vibration prediction model for a magnesite mine using four prediction variables with 20 data records. Kamali and Ataei (2010) predicted PPV in the structure of the Karoun III power plant and dam using an ANN. El Hafiz et al. (2010) evaluated ground vibration predictors using data from a single-station seismograph at a limestone quarry in Egypt. ANN prediction models have been built for one Indian coal mine and one limestone mine. Using the findings of these initial studies, it is essential to enhance the application of ANN in various mines in different Indian coal mining

Research paper thumbnail of Transformations and Microstructures-Design of the directly air-cooled pearlite-free multiphase steel from CCT diagrams developed using ANN and dilatometric methods

Research paper thumbnail of Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms

Applied Soft Computing, 2012

To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as... more To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of various metallic fillers in particulate reinforced flexible mould material composites, multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Here we have adopted two EA-based algorithms namely NSGAII and SPEA2 in order to solve the present problem independently. Comparative study of the results reveals that NSGAII performs better over SPEA2 for investigating the role of metallic fillers in particulate reinforced flexible mould material composites. A recently proposed innovization procedure is also used to unveil salient properties associated with the obtained trade-off solutions. These solutions are analyzed to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mould material. Based on the findings through investigations, the optimal selection of materials is suggested including the cost implication factor.

Research paper thumbnail of Application of Genetic Programming for Evolution of Glass-Forming Ability Parameter

Research paper thumbnail of Optimization of process parameters of friction stir welded joints of marine grade AA 5083

Materials Today: Proceedings

Research paper thumbnail of Influence of Ga Doping on Multiferroic Behaviour of Modified BiMnO3-BaTiO3 Ceramics

Journal of The Institution of Engineers (India): Series D

Research paper thumbnail of Structure, dielectricity and ferroelectricity measurement of new perovskite ceramics (1-x)BaTiO3-xBiMnO3 synthesized by solid-state reaction

Materials Chemistry and Physics

Research paper thumbnail of Modelling the steel microstructure knowledge for in-silico recognition of phases using machine learning

Materials Chemistry and Physics

Research paper thumbnail of Magneto-dielectric properties of B-site doped (Fe3+, Zr4+) BiMnO3 perovskite ceramic

Materials Science and Engineering: B

Research paper thumbnail of A study on residual stress generation versus recrystallisation in FSW joint of dissimilar alloy pair of the AA5083 and HSLA steel

Science and Technology of Welding and Joining

Research paper thumbnail of Distribution of intermetallic compounds in dissimilar joint interface of AA 5083 and HSLA steel welded by FSW technique

Research paper thumbnail of Evolution of Residual Stresses in Friction Stir Welded Joints of AA7039

Journal of The Institution of Engineers (India): Series D

Research paper thumbnail of ISIJ International, Vol. 47 (2007), No. 8, pp. 1195–1203

High strength multiphase steel is the one of the commonly used steels due to its reasonable combi... more High strength multiphase steel is the one of the commonly used steels due to its reasonable combination of strength and ductility. The maximization of strength without much compromise to the ductility is an important objective in designing the alloy along with its processing parameters. The strength of low carbon steel is improved by the addition of alloying and microalloying elements including carbide formers and other precipitation hardeners, and also by modifying the microstructure through introduction of the low temperature transformation products of austenite, e.g., bainite and martensite, which generally leads to deterioration in ductility. The design of steels from fundamental principles is a rather complex task. The determination of mechanical properties of steels in terms of their composition and process parameters requires adequate models, which can suggest a tailored change in the variables to achieve a certain target property. Thermomechanical processing is known to impr...

Research paper thumbnail of New training strategies for neural networks with application to quaternary Al–Mg–Sc–Cr alloy design problems

Applied Soft Computing, 2016

Display Omitted The training of a neural network in multiple stages.A dual stage multi-resource d... more Display Omitted The training of a neural network in multiple stages.A dual stage multi-resource data training scheme using multi-objective genetic algorithm.Development of efficient neural network model focusing on missing, but most informative domains of the dataset.The scheme is used for Al-Mg-Cr-Sc alloy system. This study concerns the training of a neural network in multiple stages considering minimization of errors from multiple data/pattern resources. The paper proposed a dual stage multi-resource data training scheme using multi-objective genetic algorithm. The training scheme has been used for the design and development of efficient neural network model focusing on missing, but most informative domains of the data set by means of introducing only a few patterns from missing domain treated separately during the later stage of training. The trained model has been used to design a quaternary Al-Mg-Cr-Sc alloy system, from the information subsets of binary Al-Cr and the ternary Al-Mg-Sc alloys. The validity of the proposed algorithm has been discussed in light of the evolution of the ageing characteristics of the new aluminium alloy system.

Research paper thumbnail of Influence of frictional heat spread pattern on the formation of intermetallic layers at the dissimilar FSW joint interface between AA 5083 and HSLA steel

Journal of Manufacturing Processes

Research paper thumbnail of Synthesis of novel nanostructured 0.6BMO-0.4BT perovskite ceramic and its thermal, structural and mechanical characteristics

Materials Today: Proceedings

Research paper thumbnail of Investigation of the thermal properties of Cu–Ag core-shell nanowires using molecular dynamics simulation

Physica B: Condensed Matter

Research paper thumbnail of Hierarchical Oxide Nanostructures-Based Gas Sensor: Recent Advances

Materials Horizons: From Nature to Nanomaterials, 2020

Chemoresistive sensors based on metal oxide semiconductor (MOS) materials have been extensively i... more Chemoresistive sensors based on metal oxide semiconductor (MOS) materials have been extensively investigated for the sake of identifying various toxic, hazardous, and explosive gases owing to their exceptional benefits, for example, such as outstanding sensitivity, cost-effectiveness, ease of fabrication, and facile integration. Over the years, it has been understood that factors that signifies sensing properties such as gas response, selectivity, stability, and promptness to response/recovery relies upon materials and morphology sensing materials, transducer designs, and few other factors. However, sluggish response, selectivity issues in real environment, higher operational temperature, and thermomechanical stability limits its widespread applications in the field of gases as well as organic vapor monitoring. In view of the above challenges, severe efforts have been made by scientific community to offset the as-described deficiencies by multiple strategies. In this aspect, oxide-based hierarchical nanostructures display strong interactions with the reacting species, eventually yielding outstanding sensing properties in comparison to other simple nano- and/or microstructures. A vast range of diverse morphologies and nano-, micro-, or mesoscale structures have been studied during recent past, each one revealing promising sensing properties toward specific chemical compounds. This chapter summarizes a comprehensive database based on previous notable works as well as latest developments in the synthesis, fabrication, and characterization of hierarchical metal oxide-based gas sensors for multiple applications.

Research paper thumbnail of Sintering effect on the structure and multiferroic behavior of nanostructured BiMnO3 ceramic synthesized by mechanochemical route

Ferroelectrics, 2021

Abstract BiMnO3 (BMO) ceramics was synthesized and sintered by mechanochemical method. Effect of ... more Abstract BiMnO3 (BMO) ceramics was synthesized and sintered by mechanochemical method. Effect of sintering on the structure and multiferroic was investigated by XRD, FESEM, HRTEM, ferroelectric and magnetic study. These studies synchronously exhibited the formation of distinct nanostructured monoclinic perovskite (C2 space group) phase with crystallite size range from 5 to 40 nm. The dielectric study revealed a significant dependence of εr and tanδ with the change in sintering temperature. P-E loop and magnetization studies revealed a trend of increase in Pr (0.396 μC/cm2 to 0.50 μC/cm2) and decrease in Ms (0.125 to 0.085) respectively with the rise in sintering temperature.

Research paper thumbnail of Effect of oxygen vacancies on the dielectricity of Ga doped equimolar BiMnO3–BaTiO3 characterized by XPS analysis

Physica B: Condensed Matter, 2021

Research paper thumbnail of Development of a blast-induced vibration prediction model using an artificial neural network

Journal of the Southern African Institute of Mining and Metallurgy, 2019

In an opencast coal mine explosives are used for fragmentation of coal and overburden. If the exp... more In an opencast coal mine explosives are used for fragmentation of coal and overburden. If the explosive energy is not fully utilized it causes blast-induced ground vibration, which may damage nearby structures. Ground vibration is expressed as peak particle velocity (PPV). During different stages of mine planning and operation, it is necessary to use a ground vibration prediction model for blasthole design. Selection of the modelling technique is crucial. Mathematical and statistical modelling techniques have limited application because of the lack of explicit knowledge about the complex mine blasting system. Vogiatzi (2002) highlighted the problem of multicollinearity in case of statistical modeling techniques. Mutalib et al. (2013) stated that mathematical models are unable to capture the nonlinear relationship between several blasting-related parameters due to the complexity of the model input data. However, the difficulty involved in modelling complex blast vibration problems can be removed by adopting an alternative soft computing modelling approach. One of the soft computing techniques is the artificial neural network (ANN). Ragam and Nimaje (2018) developed an ANN model for predicting PPV using six input variables. Kosti et al. (2013) stated that the conventional predictors fail to provide acceptable prediction accuracy. They showed that a neural network model with four mine blast parameters as input could make significantly more accurate on-site predictions. Sayadi et al., (2013), using a database from Teheran Cement Company limestone mines, found that a neural network resulted in maximum accuracy and minimum error. Khandelwal and Singh (2009) developed an ANN model using 150 data records from an Indian coal mine with site-specific rock characteristics and geomining setting. Khandewal and Singh (2007) built a ground vibration prediction model for a magnesite mine using four prediction variables with 20 data records. Kamali and Ataei (2010) predicted PPV in the structure of the Karoun III power plant and dam using an ANN. El Hafiz et al. (2010) evaluated ground vibration predictors using data from a single-station seismograph at a limestone quarry in Egypt. ANN prediction models have been built for one Indian coal mine and one limestone mine. Using the findings of these initial studies, it is essential to enhance the application of ANN in various mines in different Indian coal mining

Research paper thumbnail of Transformations and Microstructures-Design of the directly air-cooled pearlite-free multiphase steel from CCT diagrams developed using ANN and dilatometric methods

Research paper thumbnail of Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms

Applied Soft Computing, 2012

To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as... more To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of various metallic fillers in particulate reinforced flexible mould material composites, multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Here we have adopted two EA-based algorithms namely NSGAII and SPEA2 in order to solve the present problem independently. Comparative study of the results reveals that NSGAII performs better over SPEA2 for investigating the role of metallic fillers in particulate reinforced flexible mould material composites. A recently proposed innovization procedure is also used to unveil salient properties associated with the obtained trade-off solutions. These solutions are analyzed to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mould material. Based on the findings through investigations, the optimal selection of materials is suggested including the cost implication factor.

Research paper thumbnail of Application of Genetic Programming for Evolution of Glass-Forming Ability Parameter

Research paper thumbnail of Optimization of process parameters of friction stir welded joints of marine grade AA 5083

Materials Today: Proceedings

Research paper thumbnail of Influence of Ga Doping on Multiferroic Behaviour of Modified BiMnO3-BaTiO3 Ceramics

Journal of The Institution of Engineers (India): Series D

Research paper thumbnail of Structure, dielectricity and ferroelectricity measurement of new perovskite ceramics (1-x)BaTiO3-xBiMnO3 synthesized by solid-state reaction

Materials Chemistry and Physics

Research paper thumbnail of Modelling the steel microstructure knowledge for in-silico recognition of phases using machine learning

Materials Chemistry and Physics