An artificial neural network(ANN) constitutive model and JohnsoneC ook(Je C) model were developed for 7017 aluminium alloy based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments a...An artificial neural network(ANN) constitutive model and JohnsoneC ook(Je C) model were developed for 7017 aluminium alloy based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments at various temperatures. A neural network configuration consists of both training and validation, which is effectively employed to predict flow stress. Temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model was performed. It was observed that the developed neural network model could predict flow stress under various strain rates and temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB over a range of temperatures(25 e300 C), strains(0.05e0.3) and strain rates(1500e4500 s 1) were employed to formulate JeC model to predict the flow stress behaviour of 7017 aluminium alloy under high strain rate loading. The JeC model and the back-propagation ANN model were developed to predict the flow stress of 7017 aluminium alloy under high strain rates, and their predictability was evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the J-C model are found to be 0.8461 and 10.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. The predictions of ANN model are observed to be in consistent with the experimental data for all strain rates and temperatures.展开更多
An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) exper...An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures(500e650 C), strains(0.05e0.2) and strain rates(1000e5500/s) are employed to formulate Je C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the Je C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.展开更多
Present study focuses on the terminal penetration of tungsten heavy alloy(WHA) long rod penetrator impacted against armour steel at an impact velocity of 1600 m/s. The residual penetrator and armour steel target recov...Present study focuses on the terminal penetration of tungsten heavy alloy(WHA) long rod penetrator impacted against armour steel at an impact velocity of 1600 m/s. The residual penetrator and armour steel target recovered after the ballistic test have been characterized using optical microscope, scanning electron microscope(SEM) and electron probe micro analyzer(EPMA). Metallurgical changes in target steel and WHA remnant have been analysed. Large shear stresses and shear localization have resulted in local failure and formation of erosion products. Severe plastic deformation acts as precursor for formation of adiabatic shear band(ASB) induced cracks in target steel. Recovered WHA penetrator remnant also exhibits severe plastic deformation forming localized shear bands, ASB induced cracks and shock induced cracks.展开更多
The present work discusses the experimental study on wire-cut electric discharge machining of hot-pressed boron carbide.The effects of machining parameters,such as pulse on time(TON),peak current(IP),flushing pressure...The present work discusses the experimental study on wire-cut electric discharge machining of hot-pressed boron carbide.The effects of machining parameters,such as pulse on time(TON),peak current(IP),flushing pressure(FP) and spark voltage on material removal rate(MRR)and surface roughness(R_a) of the material,have been evaluated.These parameters are found to have an effect on the surface integrity of boron carbide machined samples.Wear rate of brass wire increases with rise in input energy in machining of hot-pressed boron carbide.The surfaces of machined samples were examined using scanning electron microscopy(SEM).The influence of machining parameters on mechanism of MRR and R_a was described.It was demonstrated that higher TON and peak current deteriorate the surface finish of boron carbide samples and result in the formation of large craters,debris and micro cracks.The generation of spherical particles was noticed and it was attributed to surface tension of molten material.Macro-ridges were also observed on the surface due to protrusion of molten material at higher discharge energy levels.展开更多
基金Defence Research and Development Organization, India for financial help in carrying out the experiments
文摘An artificial neural network(ANN) constitutive model and JohnsoneC ook(Je C) model were developed for 7017 aluminium alloy based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments at various temperatures. A neural network configuration consists of both training and validation, which is effectively employed to predict flow stress. Temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model was performed. It was observed that the developed neural network model could predict flow stress under various strain rates and temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB over a range of temperatures(25 e300 C), strains(0.05e0.3) and strain rates(1500e4500 s 1) were employed to formulate JeC model to predict the flow stress behaviour of 7017 aluminium alloy under high strain rate loading. The JeC model and the back-propagation ANN model were developed to predict the flow stress of 7017 aluminium alloy under high strain rates, and their predictability was evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the J-C model are found to be 0.8461 and 10.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. The predictions of ANN model are observed to be in consistent with the experimental data for all strain rates and temperatures.
文摘An artificial neural network(ANN) constitutive model is developed for high strength armor steel tempered at 500 C, 600 C and 650 C based on high strain rate data generated from split Hopkinson pressure bar(SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnsone Cook(Je C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stressestrain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures(500e650 C), strains(0.05e0.2) and strain rates(1000e5500/s) are employed to formulate Je C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient(R) and average absolute relative error(AARE). R and AARE for the Je C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.
基金Defence Research Development Organization(DRDO)for financial support to carry out this work at Defence Metallurgical Research Laboratory
文摘Present study focuses on the terminal penetration of tungsten heavy alloy(WHA) long rod penetrator impacted against armour steel at an impact velocity of 1600 m/s. The residual penetrator and armour steel target recovered after the ballistic test have been characterized using optical microscope, scanning electron microscope(SEM) and electron probe micro analyzer(EPMA). Metallurgical changes in target steel and WHA remnant have been analysed. Large shear stresses and shear localization have resulted in local failure and formation of erosion products. Severe plastic deformation acts as precursor for formation of adiabatic shear band(ASB) induced cracks in target steel. Recovered WHA penetrator remnant also exhibits severe plastic deformation forming localized shear bands, ASB induced cracks and shock induced cracks.
文摘The present work discusses the experimental study on wire-cut electric discharge machining of hot-pressed boron carbide.The effects of machining parameters,such as pulse on time(TON),peak current(IP),flushing pressure(FP) and spark voltage on material removal rate(MRR)and surface roughness(R_a) of the material,have been evaluated.These parameters are found to have an effect on the surface integrity of boron carbide machined samples.Wear rate of brass wire increases with rise in input energy in machining of hot-pressed boron carbide.The surfaces of machined samples were examined using scanning electron microscopy(SEM).The influence of machining parameters on mechanism of MRR and R_a was described.It was demonstrated that higher TON and peak current deteriorate the surface finish of boron carbide samples and result in the formation of large craters,debris and micro cracks.The generation of spherical particles was noticed and it was attributed to surface tension of molten material.Macro-ridges were also observed on the surface due to protrusion of molten material at higher discharge energy levels.
