Using simulation to predict additive outcomes

Additive manufacturing processes such as laser powder bed fusion (LPBF) and Electron Beam Melting (EBM) deal with large scales of data. Components can have a build envelope of 30 cm, the material melt pool is only around 100 microns, while the laser needs to travel 10s of kilometres on its path to build a full part. Combined with cooling rates of 1 million degrees per second, a vast array of variables need to run seamlessly, quickly and leave little margin for error to create accurate and predictable components.

By their nature, additive processes like LPBF can create structures with more complex geometries, manipulating material properties to build them point by point. This means localised areas can vary within a single part. For example, the hardness or stiffness can vary throughout the microstructure, or pores can be included in certain regions of the part. Tailoring properties location by location in any given part can optimise the efficiency and performance of the components, but can be difficult to achieve – and produce consistently.