| Abstract Scope |
In laser powder-bed fusion additive manufacturing (LPBF-AM), part design, materials, machine and post-processing parameters are intertwined, and therefore, require iterative multi-level optimization to meet desired part performance. Ongoing work at GE Research is aimed at robust process optimization, thorough qualification and rapid insertion of additive materials. We developed a physics-informed data-driven framework for LPBF-AM that utilizes probabilistic machine learning, intelligent sampling and optimization protocols, coupled with materials science to dramatically accelerate the process development, and also provide multiple optimal solutions to meet a variety of target material properties. Additionally, to address challenges of maintaining process pedigree, storing experimental datasets, and creating user-friendly analytics, we developed a Federated Big Data Storage and Analytics platform, with the ability to link diverse, multimodal data together to enable complex analytics. In this talk, I will discuss these tools and their applications to parameter optimization for alloy screening, build-productivity, non-coventional particle size distribution and layer-thicknesses. |