Microstructural evolution and resulting stress, strain, and concentration field distribution duri... more Microstructural evolution and resulting stress, strain, and concentration field distribution during Al3X (X = Sc, Zr, Er) precipitation in Al matrix are investigated in this work using the 3D-multiphase field method. Depending on the heat treatment, modulus mismatch, lattice parameter mismatch, and interfacial free energy, precipitate developed to rhombicuboctahedron, and near cuboidal morphologies. The composition distribution and Al–Al3X transformation driving force map identified a difference in precipitation kinetics for each alloy. The precipitation mechanism in the three systems is analyzed in detail with temporal evolution plots of energy components during phase transformation. Al3Er precipitate exhibits the highest growth rate due to Er's high diffusivity and significant lattice parameter mismatch in the Al–Er system. The system has a high chemical and elastic driving force for particle growth, thus attaining quasi-static equilibrium at a relatively lower temperature and...
The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-line... more The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-linear and parallel computing power of the brain. Once a neural network is significantly trained it can predict the output results in the same knowledge domain. In the present work, ANN models are developed for the simulation of compressive properties of closed-cell aluminum foam: plateau stress, Young's modulus and energy absorption capacity. The input variables for these models are relative density, average pore diameter and cell anisotropy ratio. Database of these properties are the results of the compression tests carried out on aluminum foams at a constant strain rate of 1 Â 10 À3 s À1. The prediction accuracy of all the three models is found to be satisfactory. This work has shown the excellent capability of artificial neural network approach for the simulation of the compressive properties of closed-cell aluminum foam.
: In the present work the effect of different durations of Post Weld Heat Treatment soaking times... more : In the present work the effect of different durations of Post Weld Heat Treatment soaking times (0.5,2,10,50 hours) are analyzed on the mechanical and microstructural properties of Cr-Mo alloy steel of ASTM A387 Gr-22,2.25% Cr-1%Mo alloy steel. These are used in high temperature services in steam generation applications, Oil and Gas industries, Thermal and Nuclear power plants etc. The work is focused on different regions of weldment like base metal, HAZ and weld metal to compare and analyze the results obtained in as-welded and different soaking times from hardness test, tensile testing and microstructural examination. Results showed that the hardness varies from weld centre line to base metal and peak hardness was found in the HAZ. The hardness is reduced to large extent when PWHT is done at 50 h. Microstructural changes were observed during different post weld heat treatments and their effect on mechanical properties and best suitable soaking times were reported.
ABSTRACTMetallic glasses lack long-range translational symmetry and have excess volume trapped wi... more ABSTRACTMetallic glasses lack long-range translational symmetry and have excess volume trapped within their amorphous structure, which has a direct bearing on their physical properties including deformation characteristics. Moreover, the trapped excess free volume is directly correlated to the defect concentration facilitating the possibility to model the temperature and time dependence of the free volume changes during creep as a trade off between defect generation and annihilation. Using differential scanning calorimetry (DSC) analysis the residual free volume of a metallic glass can be characterised based on the glass transition peak height (Δcp). In the present work constant strain rate tests were carried out at the ‘onset’ (Tgon = 685 K) and ‘point of inflection’ (Tgp = 705 K) of the calorimetric glass transition to study the time dependent flow behaviour in Zr55Cu30Al10Ni5 bulk metallic glass. Modelling based on DSC analysis and positron lifetime spectroscopy on samples creep deformed to different plastic strain values corroborate the stress decrease after the peak stress (‘stress overshoot’) occurring in bulk metallic glasses with increasing plastic strain to be associated with a small increase in free volume.
The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-line... more The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-linear and parallel computing power of the brain. Once a neural network is significantly trained it can predict the output results in the same knowledge domain. In the present work, ANN models are developed for the simulation of compressive properties of closed-cell aluminum foam: plateau stress, Young's modulus and energy absorption capacity. The input variables for these models are relative density, average pore diameter and cell anisotropy ratio. Database of these properties are the results of the compression tests carried out on aluminum foams at a constant strain rate of 1 Â 10 À3 s À1 . The prediction accuracy of all the three models is found to be satisfactory. This work has shown the excellent capability of artificial neural network approach for the simulation of the compressive properties of closed-cell aluminum foam.
Microstructural evolution and resulting stress, strain, and concentration field distribution duri... more Microstructural evolution and resulting stress, strain, and concentration field distribution during Al3X (X = Sc, Zr, Er) precipitation in Al matrix are investigated in this work using the 3D-multiphase field method. Depending on the heat treatment, modulus mismatch, lattice parameter mismatch, and interfacial free energy, precipitate developed to rhombicuboctahedron, and near cuboidal morphologies. The composition distribution and Al–Al3X transformation driving force map identified a difference in precipitation kinetics for each alloy. The precipitation mechanism in the three systems is analyzed in detail with temporal evolution plots of energy components during phase transformation. Al3Er precipitate exhibits the highest growth rate due to Er's high diffusivity and significant lattice parameter mismatch in the Al–Er system. The system has a high chemical and elastic driving force for particle growth, thus attaining quasi-static equilibrium at a relatively lower temperature and...
The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-line... more The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-linear and parallel computing power of the brain. Once a neural network is significantly trained it can predict the output results in the same knowledge domain. In the present work, ANN models are developed for the simulation of compressive properties of closed-cell aluminum foam: plateau stress, Young's modulus and energy absorption capacity. The input variables for these models are relative density, average pore diameter and cell anisotropy ratio. Database of these properties are the results of the compression tests carried out on aluminum foams at a constant strain rate of 1 Â 10 À3 s À1. The prediction accuracy of all the three models is found to be satisfactory. This work has shown the excellent capability of artificial neural network approach for the simulation of the compressive properties of closed-cell aluminum foam.
: In the present work the effect of different durations of Post Weld Heat Treatment soaking times... more : In the present work the effect of different durations of Post Weld Heat Treatment soaking times (0.5,2,10,50 hours) are analyzed on the mechanical and microstructural properties of Cr-Mo alloy steel of ASTM A387 Gr-22,2.25% Cr-1%Mo alloy steel. These are used in high temperature services in steam generation applications, Oil and Gas industries, Thermal and Nuclear power plants etc. The work is focused on different regions of weldment like base metal, HAZ and weld metal to compare and analyze the results obtained in as-welded and different soaking times from hardness test, tensile testing and microstructural examination. Results showed that the hardness varies from weld centre line to base metal and peak hardness was found in the HAZ. The hardness is reduced to large extent when PWHT is done at 50 h. Microstructural changes were observed during different post weld heat treatments and their effect on mechanical properties and best suitable soaking times were reported.
ABSTRACTMetallic glasses lack long-range translational symmetry and have excess volume trapped wi... more ABSTRACTMetallic glasses lack long-range translational symmetry and have excess volume trapped within their amorphous structure, which has a direct bearing on their physical properties including deformation characteristics. Moreover, the trapped excess free volume is directly correlated to the defect concentration facilitating the possibility to model the temperature and time dependence of the free volume changes during creep as a trade off between defect generation and annihilation. Using differential scanning calorimetry (DSC) analysis the residual free volume of a metallic glass can be characterised based on the glass transition peak height (Δcp). In the present work constant strain rate tests were carried out at the ‘onset’ (Tgon = 685 K) and ‘point of inflection’ (Tgp = 705 K) of the calorimetric glass transition to study the time dependent flow behaviour in Zr55Cu30Al10Ni5 bulk metallic glass. Modelling based on DSC analysis and positron lifetime spectroscopy on samples creep deformed to different plastic strain values corroborate the stress decrease after the peak stress (‘stress overshoot’) occurring in bulk metallic glasses with increasing plastic strain to be associated with a small increase in free volume.
The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-line... more The design of artificial neural network (ANN) is motivated by analogy of highly complex, non-linear and parallel computing power of the brain. Once a neural network is significantly trained it can predict the output results in the same knowledge domain. In the present work, ANN models are developed for the simulation of compressive properties of closed-cell aluminum foam: plateau stress, Young's modulus and energy absorption capacity. The input variables for these models are relative density, average pore diameter and cell anisotropy ratio. Database of these properties are the results of the compression tests carried out on aluminum foams at a constant strain rate of 1 Â 10 À3 s À1 . The prediction accuracy of all the three models is found to be satisfactory. This work has shown the excellent capability of artificial neural network approach for the simulation of the compressive properties of closed-cell aluminum foam.
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