Hot Deformation Behavior Of Ti Research Articles

Hot deformation behavior of Ti–6.0Al–7.0Nb biomedical alloy by using processing map

Abstract Ti–6.0Al–7.0Nb is a dual phase biomedical alloy used for artificial bone. Isothermal compression tests of Ti–6.0Al–7.0Nb biomedical alloy has been taken carried out on a Gleebe 3500 simulator at the strain rates of 0.001–1.0 s1 and temperatures of 750–900 °C. A constitutive equation represented as a function of temperature, strain rate and true strain was developed, and the hot deformation apparent activation energy is calculated about 341 kJ/mol. Processing maps were constructed on the basis of the experimental data for evaluation of the flow instability regime and optimization of processing parameters. Processing maps predict a single flow instability region occurred around the temperature range of 750–770 °C and the strain rate range of 0.03–1.0 s−1. The power dissipation maps at different strains exhibit similar features indicating that the processes involved in hot working have very short transient and are essentially of steady–state type. Ti–6.0Al–7.0Nb biomedical alloy can be deformed at the condition of (Topi: 850 °C, e opi : 1.0 s−1) with the peak η of 0.46 to obtain fine kinked and globular α phase for artificial bone.

Hot compressive behavior of Ti–3.0Al–3.7Cr–2.0Fe–0.1B titanium alloy

The hot deformation behavior of Ti–3.0Al–3.7Cr–2.0Fe–0.1B (TACFB) titanium alloy was investigated using a Gleeble-1500D thermal simulator in the temperature range of 800–950 °C, at constant strain rate from 0.01 s−1 to 10 s−1 and with height reduction of 70%. Flow stress and microstructure evolution during hot compression of TACFB alloy were investigated. The processing map of TACFB alloy was obtained. The results indicate that the hot deformation behavior of TACFB alloy is sensitive to the deformation temperature and strain rate. The peak flow stress decreases with increasing the test temperature and decreasing the strain rate. The constitutive relationship of TACFB alloy was obtained on the base of Arrhenius equations. When the strain rates are higher than 1.0 s−1, the dynamic recrystallization occurs, and the higher the strain rates are, the more the recrystallization is.

Hot deformation behavior of Ti–5.0Al–2.40Sn–2.02Zr–3.86Mo–3.91Cr alloy with an initial lamellar microstructure in the α + β phase field

Hot deformation behavior of Ti–5.0Al–2.40Sn–2.02Zr–3.86Mo–3.91Cr alloy with an initial lamellar microstructure in the α+β phase field was investigated at the temperatures of 1050–1130K (all below the β-transus temperature) and the strain rates of 0.001–10.0s−1 using processing maps. The apparent activation energy of deformation was calculated to be 313kJmol−1, and a constitutive equation by which the flow stress is represented as a function of strain rate and deformation temperature was developed. A processing map was constructed based on the experimental data for evaluation of the efficiency of power dissipation (η), identification of the instability regions and optimization of the α+β forging process parameters. It is found that the hot deformation at low temperature has high η value, and the microstructure obtained at high temperature is more homogeneous. The globularization process represents the moderate η value and contributes to the grain refinement. In order to obtain the homogeneous microstructure with fine grain, hot deformation should be carried out under the condition of (Topi: 1130K, ε˙opi:0.001 s−1). Flow instability is expected to occur at a single region with a higher strain rate (ε˙≥3.0 s−1) across the temperature range (Td: 1050–1130K) due to the possible occurrence of adiabatic shear banding or/and flow localization.

High Temperature Deformation Behavior of Ti-6Al-4V Alloy with an Equiaxed Microstructure: a Neural Networks Analysis

The hot deformation behavior of Ti−6Al−4V alloy with an equiaxed microstructure was investigated by means of Artificial Neural Networks (ANN). The flow stress data for the ANN model training was obtained from compression tests performed on a thermo-mechanical simulator over a wide range of temperature (from 700°C to 1100°C) with strain rates of 0.0001 s−1 to 100 s−1 and true strains of 0.1 to 0.6. It was found that the trained neural network could reliably predict flow stress for unseen data. Workability was evaluated by means of processing maps with respect to strain, strain rate, and temperature. Processing maps were constructed at different strains by utilizing the flow stress predicted by the model at finer intervals of strain rates and temperatures. The specimen failures at various instances were predicted and confirmed by experiments. The results establish that artificial neural networks can be effectively used for generating a more reliable processing map for industrial applications. A graphical user interface was designed for ease of use of the model.