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| 1 | Enhancement of wear and ballistic resistance of armour grade AA7075 aluminium alloy using friction stir processing显示文摘Industrial applications of aluminium and its alloys are restricted because of their poor tribological properties. Thermal spraying, laser surfacing, electron beam welding are the most widely used techniques to alter the surface morphology of base metal. Preliminary studies reveal that the coating and layering of aluminium alloys with ceramic particles enhance the ballistic resistance. Furthermore, among aluminium alloys,7075 aluminium alloy exhibits high strength which can be compared to that of steels and has profound applications in the designing of lightweight fortification structures and integrated protection systems. Having limitations such as poor bond integrity, formation of detrimental phases and interfacial reaction between reinforcement and substrate using fusion route to deposit hard particles paves the way to adopt friction stir processing for fabricating surface composites using different sizes of boron carbide particles as reinforcement on armour grade 7075 aluminium alloy as matrix in the present investigation. Wear and ballistic tests were carried out to assess the performance of friction stir processed AA7075 alloy. Significant improvement in wear resistance of friction stir processed surface composites is attributed to the change in wear mechanism from abrasion to adhesion. It has also been observed that the surface metal matrix composites have shown better ballistic resistance compared to the substrate AA7075 alloy. Addition of solid lubricant Mo S2 has reduced the depth of penetration of the projectile to half that of base metal AA7075 alloy. For the first time, the friction stir processing technique was successfully used to improve the wear and ballistic resistances of armour grade high strength AA7075 alloy. | I.SUDHAKAR V.MADHU G.MADHUSUDHAN REDDY K.SRINIVASA RAO | 2015 | Defence Technology(防务技术)2015，11，1： | 7 |
| 2 | Prediction of flow stress of 7017 aluminium alloy under high strain rate compression at elevated temperatures显示文摘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. | Ravindranadh BOBBILI B.RAMAKRISHNA V.MADHU A.K.GOGIA | 2015 | Defence Technology(防务技术)2015，11，1： | 6 |
| 3 | Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression显示文摘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. | Ravindranadh BOBBILI V.MADHU A.K.GOGIA | 2014 | Defence Technology(防务技术)2014，10，4： | 3 |
| 4 | Development of cost effective personnel armour through structural hybridization显示文摘The objective of the present study is to develop cost effective thermoplastic hybrid laminate using Dyneema®HB50 and Tensylon®HSBD 30A through structural hybridization method.Laminates having 20 mm thickness were fabricated and subjected to 7.6239 mm mild steel core projectile with an impact velocity of 730±10 ms1.Parameters such as energy absorption,back face deformation and rate of back face deformation were measured as a function of hybridization ratio.It was observed that hybrid laminate with 50:50 ratio(w/w)of Tensylon®and Dyneema®with Tensylon®as front face showed 200%more energy absorption when compared to 100%Tensylon®laminate and showed equal energy absorption as that of expensive 100%Dyneema®laminate.Moreover,hybrid laminate with TD50:50 ratio showed 40%lower in terms of final back face deformation than Dyneema®laminate.Rate of back face deformation was also found to be slow for hybrid laminate as compared to Dyneema®laminate.Dynamic mechanical analysis showed that,Tensylon®laminate has got higher stiffness and lower damping factor than Dyneema®and hybrid laminates.The interface between Tensylon®and Dyneema®layers was found to be separating during the penetration process due to the poor interfacial bonding.Failure behaviour of laminates for different hybridization ratios were studied by sectioning the impacted laminates.It was observed that,the Tensylon®laminate has undergone shear cutting of fibers as major failure mode whereas the hybrid laminate showed shear cutting followed by tensile stretching,fiber pull out and delamination.These inputs are highly useful for body armour applications to design cost effective armour with enhanced performance. | P.Rama Subba Reddy T.Sreekantha Reddy I.Srikanth Juhi Kushwaha V.Madhu | 2020 | Defence Technology(防务技术)2020，16，6： | 2 |
| 5 | An experimental investigation of wire electrical discharge machining of hot-pressed boron carbide显示文摘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. | Ravindranadh BOBBILI V.MADHU A.K.GOGIA | 2015 | Defence Technology(防务技术)2015，11，4： | 0 |
| 6 | Flash X-ray radiography technique to study the high velocity impact of soft projectile on E-glass/epoxy composite material显示文摘In the present paper, the high velocity impact of 9 mm soft lead projectile on 10 mm and 30 mm thick Eglass/epoxy composites was studied using a 450 kV Flash X-ray radiography(FXR) system. The basic parameters of FXR imaging, such as effect of ratio of target to film(TF) and source to target(ST) distances and X-ray penetration thickness of the composite material were optimized based on clarity and the actual dimensions of the objects. The optimized parameters were used in the FXR imaging of the ballistic event of 9 mm soft projectile on E-glass/epoxy composite. The real time deformation patterns of both the projectile and composite target during the ballistic impact were captured and studied at different time intervals. The notable failure modes of the 10 mm thick target with time include fibre breakage, bulging on the back side, delamination, recovery of the bulging, reverse bulging and its recovery. However, with increase in thickness of the target to 30 mm the only failure mechanism observed is the breaking of fibres. The ballistic impact event was also numerically simulated using commercially available LS-DYNA software. The numerically simulated deformation patterns of the projectile and target at different time intervals are closely matching with the corresponding radiographic images. | B.Venkata Ramudu C.Jayarami Reddy V.Madhu | 2019 | Defence Technology(防务技术)2019，15，2： | 0 |
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