Data to Aid Materials Design and Process Optimization of Intermetallics Based Materials

Alloys are complex systems where microstructure and properties depend on both processing conditions and chemical composition. Such variations are not typically reflected in handbook data and repositories that tend to be limited in the scope of materials covered (their compositions) or the temperature ranges (processing conditions) or lack of time dependence. As such, the engineering simulations which depend on these data are limited, especially for cases involving novel materials or new processes and often it becomes necessary to go and measure the needed data or live with the uncertainty.<br/> <br/>The CALPHAD approach captures the composition and temperature dependence of properties, as well as their temporal evolution, for industrial multicomponent alloys. As a result, data can be calculated for materials or conditions where there are gaps in the measured data. Additionally, location specific properties can be predicted and optimized for a part, which means that manufacturers will no longer be restricted to design minimums.<br/> <br/>CALPHAD simulations can be used to complement compilations or repositories of measured data, improve machine learning models, and can also be used as input into engineering codes that require more reliable materials property data. This applies to alloys in general, including alloys that contain intermetallic phases. In this presentation we introduce the CALPHAD approach and exemplify how CALPHAD based tools can be efficiently used to aid materials design and process optimization of intermetallics based materials. We will focus on Additive Manufacturing (AM) of intermetallics based materials, such as Titanium Aluminides and Ni-base superalloys. It will be demonstrated that in order to successfully be able to simulate this process and make predictions, for examples in the form of printability maps, it’s necessary to not only include sufficient physics in the simulations, but also to input accurate composition and temperature dependent thermochemical and thermophysical data.