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

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.

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 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.

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(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.

Hot deformation behavior and processing maps of Ti-15Al-12Nb alloy

The isothermal hot compression tests of Ti-15Al-12Nb alloy under wide range of strain rates(0.01-10.00 s^(-1)and deformation temperatures(950,1000,1050,and 1100℃)were carried out using Gleeble-3500 thermo-simulation machine.A constitutive equation represented as a function of temperature,strain rate and true strain was developed,and the hot deformation appar-ent activation energy is calculated to be about 453 kJ·mol^(-1).By employing dynamic material model(DMM),the processing maps of Ti-15Al-12Nb alloy at various strains were established.Maximum efficiency of about 57%for power dissipation is obtained at high temperature and low strain rate.Owing to the high power dissipation efficiency and excellent processing ability in dynamic recrystallization(DRX)zone for metal material,the optimum processing conditions are selected as the temperature range of 1050-1100℃and the strain rate range of 0.01-0.10 s^(-1).Using transmission electron microscopy(TEM)studies,it is found that the dislocation density is directly associated with the value of processing efficiency.It is observed that when the processing effi-ciency is about 22%,the dislocation density is reasonably large.The flow instability region occurs at strain rate of 10.00 s^(-1)with cracks,which should be avoided during hot processing to obtain the required mechanical properties.