Abstract Scope |
A novel modeling strategy, which combines artificial intelligence (AI) with artificial materials (AM) will be proposed and called Artificial Materials Intelligence (AMI). We define AMI as a hybrid physically-based data-driven modeling strategy that uses well-established AI-components, such as mathematical algorithms, statistics, machine and deep learning, computer vision, robotics, and others to analyze heterogeneous simulated and experimental data on different modeling scales. To build the so-called creep indicator model (CIM) using AMI modeling strategy, physically-based and data-driven models are combined to identify statistically sound correlations between materials chemistry, thermodynamics, microstructures and mechanical data of single crystal Ni- and Co-based super alloys. The proposed CIM allow us to identify the influence of individual physical effects from the considered contributions on selected material properties. Moreover, they are frequently required to accelerate the computer-assisted design of new materials and alloys, for example, the development of rhenium-free Ni-based. |