High-temperature Titanium Alloys

The development of high-temperature titanium alloys has contributed significantly to the spectacular progress in thrust-to-weight ratio of the aero gas turbines. This paper presents anoverview on the development of high-temperature titanium alloys used in aero engines and potential futuristic materials based on titanium aluminides and composites. The role of alloychemistry, processing, and microstructure, in determining the mechanical properties of titanium alloys is discussed. While phase equilibria and microstructural stability consideration haverestricted the use of conventional titanium alloys up to about 600 "C, alloys based on TiPl (or,), E,AINb (0), TiAl (y), and titaniumltitanium aluminides-based composites offer a possibility ofquantum jump in the temperature capability of titanium alloys

Selecting Appropriate Metallic Alloy for Marine Gas Turbine Engine Compressor Components

Metallic alloys with excellent structural and chemical properties play a significant role in a variety of applications. The selection of suitable metallic materials for marine gas turbine engine components is a real challenge as the surrounding environment is highly corrosive and the components have to function for a designed period at varied elevated temperatures. This chapter explains the selection of a suitable metallic alloy for marine gas turbine engine compressor section components based on extensive experimental data on two titanium‐based alloys 6242 and IMI 834 under simulated marine gas turbine engine environmental conditions. The results revealed that 6242 exhibits superior performance over IMI 834. Therefore, the titanium alloy 6242 in association with appropriate protective coating is recommended to fabricate components intended to use for marine gas turbine engine compressor section applications

Dynamic Properties of RHA Steel under Planar Shock Loading using Explosive Driven Plate Impact System

Planar shock loading of rolled homogeneous armour (RHA) steel has been studied at high pressures in the range of 20-100 GPa using an explosive-driven plate impact system. Shock velocities and flyer velocities are measured using time of arrival pins embedded in the target at known depths. The shock equation of state of RHA steel has been determined. α → ε phase transition stress and hugoniot elastic limit (HEL) of RHA steel have been determined through manganin gauge and found to be 12.2 ±0.6 GPa and 4.1 ± 0.2 GPa, respectively. The experimental stress of phase transition has been compared with the stress calculated using ThermoCalc software. The shock properties have been incorporated in the Autodyn simulation package and simulations were performed to determine flyer velocity, pressures and the results are compared with that of experiments.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.196-202, DOI: http://dx.doi.org/10.14429/dsj.65.795

Multi response optimization of wire-EDM process parameters of ballistic grade aluminium alloy

In the current investigation, a multi response optimization technique based on Taguchi method coupled with Grey relational analysis is planned for wire-EDM operations on ballistic grade aluminium alloy for armour applications. Experiments have been performed with four machining variables: pulse-on time, pulse-off time, peak current and spark voltage. Experimentation has been planned as per Taguchi technique. Three performance characteristics namely material removal rate (MRR), surface roughness (SR) and gap current (GC) have been chosen for this study. Results showed that pulse-on time, peak current and spark voltage were significant variables to Grey relational grade. Variation of performance measures with process variables was modelled by using response surface method. The confirmation tests have also been performed to validate the results obtained by Grey relational analysis and found that great improvement with 6% error is achieved

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

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 Johnson–Cook (J–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 stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C), strains (0.05–0.2) and strain rates (1000–5500/s) are employed to formulate J–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 J–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

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

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 (Ra) 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 Ra 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

Modelling and analysis of material removal rate and surface roughness in wire-cut EDM of armour materials

The current work presents a comparative study of wire electrical discharge machining (WEDM) of armour materials such as aluminium alloy 7017 and rolled homogeneous armour (RHA) steel using buckingham pi theorem to model the input variables and thermo-physical characteristics of WEDM on material removal rate (MRR) and surface roughness (Ra) of Al 7017 and RHA steel. The parameters of the model such as pulse-on time, flushing pressure, input power, thermal diffusivity and latent heat of vaporization have been determined through design of experiment methodology. Wear rate of brass wire increases with rise in input energy in machining Al 7017. The dependence of thermo-physical properties and machining variables on mechanism of MRR and Ra has been described by performing scanning electron microscope (SEM) study. The rise in pulse-on time from 0.85μs to 1.25μs causes improvement in MRR and deterioration of surface finish. The machined surface has revealed that craters are found on the machined surface. The propensity of formation of craters increases during WEDM with a higher current and larger pulse-on time

Microstructural observations on the terminal penetration of long rod projectile

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

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

An artificial neural network (ANN) constitutive model and Johnson–Cook (J–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 Johnson–Cook (J–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 stress–strain data obtained from high strain rate compression tests using SHPB over a range of temperatures (25°–300 °C), strains (0.05–0.3) and strain rates (1500–4500 s−1) were employed to formulate J–C model to predict the flow stress behaviour of 7017 aluminium alloy under high strain rate loading. The J–C 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