Predicting the quality of materials after they are subjected to plasma sintering is a challenging task because of the non-linear relationships between the process variables and mechanical properties. Furthermore, the variables governing the sintering process affect the microstructure and the mechanical properties of the final product. Therefore, an artificial neural network modeling was carried out to correlate the parameters of the spark plasma sintering process with the densification and hardness values of Ti-6Al-4V alloys dispersed with nano-sized TiN particles. The relative density (%), effective density (g/cm3), and hardness (HV) were estimated as functions of sintering temperature (oC), time (min), and composition (change in % TiN). A total of 20 datasets were collected from the open literature to develop the model. The high-level accuracy in model predictions (>80%) discloses the complex relationships among the sintering process variables, product quality, and mechanical performance. Further, the effect of sintering temperature, time, and TiN percentage on the density and hardness values were quantitatively estimated with the help of the developed model.

Additive manufacturing (AM) is a highly innovative method for joining dissimilar materials for industrial applications. In the present work, AM of STS630 and Ti-6Al-4V powder alloys on medium entropy alloys (MEAs) NiCrCo and NiCrCoMn is studied. The STS630 and Ti64 powders are deposited on the MEAs. Joint delamination and cracks are observed after the deposition of Ti64 on the MEAs, whereas the deposition of STS630 on the MEAs is successful, without any cracks and joint delamination. The microstructure around the fusion zone interface is characterized by scanning electron microscopy and X-ray diffraction. Intermetallic compounds are formed at the interfacial regions of MEA-Ti64 samples. In addition, Vicker’s hardness value increased dramatically at the joint interface between MEAs and Ti-6Al-4V compared to that between MEAs and STS630. This result is attributed to the brittle nature of the joint, which can lead to a decrease in the joint strength.

The β-transus temperature in titanium alloys plays an important role in the design of thermo-mechanical treatments. It primarily depends on the chemical composition of the alloy and the relationship between them is non-linear and complex. Considering these relationships is difficult using mathematical equations. A feed-forward neural-network model with a back-propagation algorithm was developed to simulate the relationship between the β-transus temperature of titanium alloys, and the alloying elements. The input parameters to the model consisted of the nine alloying elements (i.e., Al, Cr, Fe, Mo, Sn, Si, V, Zr, and O), whereas the model output is the β-transus temperature. The model developed was then used to predict the β-transus temperature for different elemental combinations. Sensitivity analysis was performed on a trained neural-network model to study the effect of alloying elements on the β-transus temperature, keeping other elements constant. Very good performance of the model was achieved with previously unseen experimental data. Some explanation of the predicted results from the metallurgical point of view is given. The graphical-user-interface developed for the model should be very useful to researchers and in industry for designing the thermo-mechanical treatment of titanium alloys.

Internally nitrided dilute W-Ti alloy specimens having a heavily deformed surface microstructure were prepared by a multi-step internal nitriding at 1573-2073 K. Primary nitriding below their recrystallization temperature induced a precipitation of ultrafine TiN particles. After secondary and tertiary nitriding, those precipitates grew into rod-like TiN with a length of 20-60 nm. The recrystallization temperature after nitriding was elevated above 2073 K. The yield strength at 1773 K obtained from nitrided W-0.5 mass% Ti alloy was about 5 times as large as that of the recrystallized specimen. DBTT of the nitrided alloys was about 373 K.

Titanium alloys and Titanium alloy-based particulate composites were synthesized using the blended elemental P/M route. First, processing conditions such as the fabrication of master alloy powder were investigated. Ti-6Al-4V, Ti-5Al-2.5Fe, Ti-6Al-2Sn-4Zr-2Mo, IMI685, IMI829, Timetal 1100 and Timetal 62S, and Ti-6Al-2Sn-4Zr-2Mo/ 10%TiB and Timetal 62S/10%TiB were then synthesized using the optimal processing conditions obtained. The microstructures and mechanical properties such as tensile strength and high cycle fatigue strength were evaluated.