Computational Modeling in Additive Manufacturing
Cold spray deposition requires the optimization of a multitude of parameters, which is time-consuming to achieve through experiments. Computational modeling in cold spray deposition helps transition these numerous parameters into practical solutions at a low cost and in less time. Researchers at PFL are currently working on various computational aspects of cold spray deposition, including the investigation of feedstock flow stress, optimization of process gas parameters, and microstructural modeling of cold spray deposits.
Understanding the flow stress of the feedstock powders allows us to comprehend the plastic deformation behavior during the cold spray process. Quasi-static finite element analysis is utilized to study stress-strain behavior by developing a plasticity material model and simulating nano-indentation response. In addition to powder mechanical properties, process parameters such as carrier gas type, pressure, and temperature are optimized using Kinetic Simulation Software (KSS) to create unique process maps. These optimal process maps result in high-quality coatings with improved deposition efficiency.
The microstructure of cold sprayed coatings is inherently heterogeneous and comprises features such as grains, grain boundaries, splats, and splat boundaries. These microstructural heterogeneities significantly influence the mechanical response of the coating. To predict the overall effective mechanical properties of cold sprayed coatings, microstructure-based modeling is conducted using Object-Oriented Finite Element Analysis (OOF2). OOF2 employs a multi-scale microstructure approach, ranging from the splat level (local) to the macro-scale (global), to associate local properties with the global properties of the coating.