Speaker
Description
Metal additive manufacturing (MAM) has received a significant amount of attention from different industrial sectors such as medical, aerospace, automotive, energy, etc., mainly due to its uniqueness in manufacturing of customized assembled metallic components with complex geometries. On the other hand, this process entails a wide range of input process parameters that can affect the overall mechanical properties of the final components as well as their quality. Since the process is very sensitive to the input parameters, any improper selection of these process parameters can easily lead to an unwanted defect in the final product.
To circumvent this issue, development of advanced numerical models can be very helpful in understanding the extent of the influence of these input process parameters on the quality of a component. At the same time, online monitoring of the process can be used to validate these models and in this way, the combination of modelling and monitoring will give a clear picture of the process and the mechanisms of the formation of possible defects.
In this work, two numerical models are developed, a melt pool scale and a micro-scale model. The former is based on Computational Fluid Dynamics (CFD) and is used to predict the quality of the tracks made by the process and also to determine the temperature field. The latter, while receiving the temperature from the melt pool model, simulates the grain growth at a lower dimension. Furthermore, different online monitoring measurements are carried out including IR thermal-imaging, in-situ melt pool monitoring and temperature measurement using photo-diodes. EBSD and SEM analyses are also done to validate the developed models. Using the combination of these techniques, it is intended to show how changing the laser beam size can affect the track shape and its microstructure.
| Speaker Country | Denmark |
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