Additive manufacturing (AM), widely known as 3D printing, is a direct digital manufacturing process, where a component can be produced layer by layer from 3D digital data with no or minimal use of machining, molding, or casting. AM has developed rapidly in the last 10 years and has demonstrated significant potential in cost reduction of performance-critical components. This can be realized through improved design freedom, reduced material waste, and reduced post processing steps. Modeling AM processes not only provides important insight in competing physical phenomena that lead to final material properties and product quality but also provides the means to exploit the design space towards functional products and materials. The length-and timescales required to model AM processes and to predict the final workpiece characteristics are very challenging. Models must span length scales resolving powder particle diameters, the build chamber dimensions, and several hundreds or thousands of meters of heat source trajectories. Depending on the scan speed, the heat source interaction time with feedstock can be as short as a few microseconds, whereas the build time can span several hours or days depending on the size of the workpiece and the AM process used. Models also have to deal with multiple physical aspects such as heat transfer and phase changes as well as the evolution of the material properties and residual stresses throughout the build time. The modeling task is therefore a multi-scale, multi-physics endeavor calling for a complex interaction of multiple algorithms. This paper discusses models required to span the scope of AM processes with a particular focus towards predicting as-built material characteristics and residual stresses of the final build. Verification and validation examples are presented, the over-spanning goal is to provide an overview of currently available modeling tools and how they can contribute to maturing additive manufacturing.

Powder Bed Additive Manufacturing offers unique advantages in terms of manufacturing cost, lot size and product complexity compared to traditional processes such as casting, where a minimum lot size is mandatory to achieve economic competitiveness. Many studies -both experimental and numerical -are dedicated to the analysis of how process parameters such as heat source power, scan speed and scan strategy affect the final material properties. Apart from the general urge to increase the build rate using thicker powder layers, the coating process and how the powder is distributed on the processing table has receive27d very little attention to date. This paper focuses on the first step of every powder bed build process: Coating the process table. A numerical study is performed to investigate how powder is transferred from the source to the processing table. A solid coating blade is modelled to spread commercial Ti-6Al-4V powder. The resulting powder layer is analyzed statistically to determine the packing density and its variation across the processing table. The results are compared with literature reports using so called "rain" models. A parameter study is performed to identify the influence of process table displacement and wiper velocity on the powder distribution. The achieved packing density and how that affects subsequent heat source interaction with the powder bed is also investigated numerically.

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Abstract:In powder bed based Additive Manufacturing (AM) processes like Selective Laser Melting (SLM) or Electron Beam Melting (EBM), the spatial distribution of the individual powder particles is typically unknown. Nevertheless, the distribution of particles in the heat affected zone defines the thermophysical properties of the region being processed by the heat source and therefore plays a crucial role in heat transfer processes. In this work, the spatial distribution of individual particles and their influence on the AM process is numerically investigated. Two powder bed configurations are compared: One powder bed is generated using the discrete element method (DEM) to model the coating process; the second powder bed is arranged in the BCC structure. The melting and solidification of both configurations are modelled. The predicted melt pool dimensions are compared with experimentally determined values. The results indicate that modelling the coating process is necessary to ensure accurate modelling of the heat source powder bed interaction as well as an accurate prediction of the melt pool characteristics. Keywords

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