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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.

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Titanium aluminide (TiAl) turbine blades produced by isothermal forging have recently been implemented in the low-pressure part of commercial aircraft jet engines. However, the slow speed of isothermal forging, costly molybdenum-based dies and the required protective forging atmosphere makes the process rather expensive. Currently, industrial forging is done by closed-die isothermal forging processes with stationary dies. Idle time occurs when single parts are inserted and extracted from the dies. As an interesting alternative for forging small parts, a new set-up is devised and explored in this work, i.e., batch processing. Using a die set which allow for off-line preassembly and preheating, multiple parts can be forged in one stroke. The design of the batch process was based on a new material model, which was implemented into a finite element system to identify the forging parameters. The setup of the press transport system for batch processing, as well as the results of the simulations and forging experiments are presented. A cost comparison between the new process and conventional forging with stationary dies concludes that for smaller parts such as compressor blades, batch processing offers advantages regarding productivity and cost.

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