[T17] Composite/Hybrid Materials

[17D-T17-09] In-Situ/In-Operando Approach for Topology Optimization of Structural Components by Means of Thermal Imaging, Thermal Analysis and CT Scan

A. Lim^1,2,3, P. Pathak^3,4, V. Selvamanickam^1,3,4,5, *F. C. Robles Hernandez^1,2,3,5 (1.College of Engineering, Technology Division, University of Houston, USA, 2.Department of Electrical and Computer Engineering, University of Houston, USA, 3.Advanced Manufacturing Institute, University of Houston, USA, 4.Texas Center for Superconductivity, University of Houston, USA, 5.Materials Science and Engineering, University of Houston, USA)

Keywords:Laser Direct Energy Deposition, Thermal Imaging, Porosity, Balling, Machine Learning

Here we present a material-by-design approach to produce components with high performance architectures. We propose an in-situ/in-operando heat assisted transition 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 effect and other defects that deter the integrity of the printed components. This permits ML algorithms capable of predicting the presence of defects and the component's soundness in-operando.