Grant: Discovery Launch Supplement

Additive manufacturing (AM) technologies present unique design opportunities but also pose challenges due to anisotropic behaviors and manufacturing defects such as lack-of-fusion and porosity. Multi-metallic AM parts remain relatively unexplored, necessitating comprehensive research to enhance design, manufacturing, and validation processes. The proposed research program focuses on modeling multi-metallic AM parts using advanced Finite Element Analysis (FEA) techniques to predict elastoplastic behavior while considering the layering process and the influence of defects. To date, there are no analytical relationships describing the elastoplastic behavior of multi-metallic AM parts accounting for microstructural defects. The main objective is to develop semi-empirical relationships that incorporate these effects collectively to for utilization in the early design stages. The proposed program employs novel modeling techniques to develop 3D micromechanical models based on realistic data reconstructed from micro computed tomography scanning, enabling accurate predictions of elastoplastic behaviors. Additionally, the program aims to develop user-defined subroutines within FEA to account for thermal stresses and layering/buildup directions in the AM process. These innovative modeling techniques combined represents the innovation within the numerical aspect of the program. Furthermore, the program aims at developing material damage subroutines employing competing damage criteria to consider damage and delamination in multi-metallic AM parts. In terms of experimental research, the program activities include; mechanical characterization of orthotropic behaviors, post-failure analysis using Scanning Electron Microscopy and computed tomography scans. These activities play a pivotal role in validating numerical models and offering insights into underlying microstructures facilitating the development of the innovative numerical models. Finally, the analytical research builds upon the results obtained from both numerical and experimental activities, aiming to develop semi-empirical relationships. To achieve this goal, non-linear regressions and supervised learning techniques will be applied to extend or develop elastoplastic relationships that account for defects and anisotropic behavior collectively. The outcome of this program will establish the foundation for relationships to be utilized in the early design stages of AM parts capturing the overall effect of anisotropy and volumetric defects on mechanical behavior. This will directly assist AM designers in the aerospace, automotive and medical sectors in designing optimized AM structures. In addition, the program outcomes will play a significant role reduce power requirements, material waste, and carbon footprint in the transportation sector in Canada. Furthermore, the program provides an excellent training plan for HQP in advanced numerical and characterization techniques.