In-Situ/In-Operando Approach for Topology Optimization of Structural Components by Means of Thermal Imaging, Thermal Analysis and CT Scan

Here we present a material-by-design approach to produce components with high performance architectures. We propose in-situ/in-operando heat assisted transitions to achieve specific microstructures during the 3D printing process. The approach is synergistic including Machine Learning (ML), Topology Optimization (TO) based on thermal imaging, thermal analysis in-operando and CT (Computed Tomography) scanning as post processing to evaluate the printed parts. The uniqueness of our work includes in-operando identification of cold spots due to balling effects and other defects that deter the integrity of the printed components. This permits us to use ML algorithms capable of predicting the presence of defects and the component's soundness in-operando.