The role of CALPHAD based tools in filling the materials data gaps for Materials 4.0

The role of simulation in processing and manufacturing is growing. But such simulations often depend on material properties which can vary with both processing conditions and allowable chemistry ranges. These variations are not typically reflected in handbook data and repositories which tend to be limited in the scope of materials covered (their compositions) or the temperature ranges (processing conditions) or lack of time dependence.

For more than 35 years, CALPHAD based tools have been used in the development of new alloys and improving the understanding and processing of existing alloys. CALPHAD is seen as one of the foundational components of an ICME/MGI framework. The CALPHAD approach captures the composition and temperature dependence of properties, as well as their temporal evolution, for industrial multi component alloys. As such, data can be simulated for materials or conditions where there are gaps in the measured data, including new alloy development and the introduction of new processes. Additionally, location specific properties can be predicted and optimized for a part, which means that manufacturers will no longer be restricted to design minimums.

CALPHAD simulations can be used to complement database repositories of measured data, improve machine learning models, and can also be used as input into engineering codes that require more robust materials property data.

This presentation will describe:

1. Why it will never be possible to measure experimentally all the data we need.
   
   
2. Describe what CALPHAD is and how it fits into an overall ICME/MGI framework.
   
   
3. Illustrate extensions of the CALPHAD approach to modeling thermo-physical and thermo-mechanical properties beyond its traditions in phase equilibria and thermodynamics.
   
   
4. Outline how different computational approaches such as Ab Initio, CALPHAD and Machine Learning can be used together to fill the data gaps in the materials information needed for the product life cycle.