Productivity in additive manufacturing by process and geometry simulation

Productivity in powder additive manufacturing is strongly dependent on process parameters, powder properties as well as the required component properties. In general, the standard processes established are empirical compromises between powder size, bed thickness and process parameters, aiming to reach the highest productivity (by increased build speed) and desired properties of the components. Lack of theoretical understanding on the complex interplay between micro-scale and macro-scale properties and the limited possibility to perform online measurements make it almost impossible to define an optimal process window. Therefore, there is a great need for developing novel modeling, simulation and optimization techniques for the additive manufacturing process.  

Two initiatives

1. Increased AM productivity by multiphysics process simulation 
   Metal additive manufacturing involves a number of coupled physical phenomena occurring at different scales. Therefore, a multiphysics model, which takes process parameters such as powder particle size, powder bed thickness, heat input on the local scale determined by the spot size and energy of the source (laser or electron beam), phase transition as well as scanning speed into account, is needed. However, to best utilize the simulation capabilities models with different level of detail need to be developed to gain detailed understanding of the AM process, and as an aid to choose process parameters or designing geometries, to optimize build paths and for variation simulation.
2. Geometry simulation and curve generation 
   Geometric variation is present in every manufacturing process and can lead to quality problem during production or during use. The aim is to develop fast approximate methods that enable us to simulate the effect of variation. Since previous research has shown that part variability in additive manufacturing is one of the main research challenges, new methods and tools for designing robust processes for additive manufacturing are critical.