Classification of γ-γ and γ-α2lamellar boundaries on the basis of continuity of strains and slip-twinning planes in fatigued TiAl polysynthetically twinned crystals

Improving the Tensile Properties of Additively Manufactured β-Containing TiAl Alloys via Microstructure Control Focusing on Cellular Precipitation Reaction

The effect of a two-step heat treatment on the microstructure and high-temperature tensile properties of β-containing Ti-44Al-4Cr (at%) alloys fabricated by electron beam powder bed fusion were examined by focusing on the morphology of α2/γ lamellar grains and β/γ cells precipitated at the lamellar grain boundaries by a cellular precipitation reaction. The alloys subjected to the first heat treatment step at 1573 K in the α + β two-phase region exhibit a non-equilibrium microstructure consisting of the α2/γ lamellar grains with a fine lamellar spacing and a β/γ duplex structure located at the grain boundaries. In the second step of heat treatment, i.e., aging at 1273 K in the β + γ two-phase region, the β/γ cells are discontinuously precipitated from the lamellar grain boundaries due to excess Cr supersaturation in the lamellae. The volume fraction of the cells and lamellar spacing increase with increasing aging time and affect the tensile properties of the alloys. The aged alloys exhibit higher strength and comparable elongation at 1023 K when compared to the as-built alloys. The strength of these alloys is strongly dependent on the volume fraction and lamellar spacing of the α2/γ lamellae. In addition, the morphology of the β/γ cells is also an important factor controlling the fracture mode and ductility of these alloys.

Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1

Data-driven or machine learning approaches are increasingly being used in material science and research. Specifically, machine learning has been implemented in the fields of materials discovery, prediction of phase diagrams and material modelling. In this work, the application of machine learning to the traditional phenomenological flow stress modelling of the titanium aluminide (TiAl) alloy TNM-B1 (Ti-43.5Al-4Nb-1Mo-0.1B) is investigated. Three model types were developed, analyzed and compared; a physics-based phenomenological model (PM) originally developed for steel by Cingara and McQueen, a purely data-driven machine learning model (MLM), and a hybrid model (HM), which uses characteristic points predicted by a learning algorithm as input for the phenomenological model. The same amount of data was used to both fit the PM and train the MLM and HM. The models were analyzed and compared based on the accuracy of their predictions, development and computing time, and their ability to predict on interpolated and extrapolated inputs. The results revealed that for the same amount of experimental data, the MLM was more accurate than the PM. In addition, the MLM was better able to capture the characteristic peak stress in the TNM-B1 the flow curves, and could be developed and computed faster. Furthermore, the MLM was able to make realistic predictions for inputs outside the experimental data used for training. The HM showed comparable accuracy to the PM for the experimental conditions. However, the HM was able to produce a better fit for input conditions outside the training data.