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An experimental investigation of wire electrical discharge machining of hot-pressed boron carbide

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Defence Technology（防务技术） 2015年第4期11卷 344-349页

作者： ravindranadh bobbili V.MADHU A.K.GOGIA Defence Metallurgical Research Laboratory

The present work discusses the experimental study on wire-cut electric discharge machining of hot-pressed boron *** 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 *** parameters are found to have an effect on the surface integrity of boron carbide machined *** rate of brass wire increases with rise in input energy in machining of hot-pressed boron *** surfaces of machined samples were examined using scanning electron microscopy(SEM).The influence of machining parameters on mechanism of MRR and R_a was *** 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 *** generation of spherical particles was noticed and it was attributed to surface tension of molten ***-ridges were also observed on the surface due to protrusion of molten material at higher discharge energy levels.

关键词：电火花加工碳化硼热压制实验扫描电子显微镜材料去除率表面粗糙度电火花线切割

Prediction of flow stress of 7017 aluminium alloy under high strain rate compression at elevated temperatures

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Defence Technology（防务技术） 2015年第1期11卷 93-98页

作者： ravindranadh bobbili B.RAMAKRISHNA V.MADHU A.K.GOGIA Defence Metallurgical Research Laboratory

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.

关键词： Aluminium alloy Artificial neural network Johnson-Cook model

Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

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Defence Technology（防务技术） 2014年第4期10卷 334-342页

作者： ravindranadh bobbili V.MADHU A.K.GOGIA Defence Metallurgical Research Laboratory Hyderabad 500058 India

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.

关键词：人工神经网络模型高应变率高强度装甲钢流变应力可预测性压缩评估