Pores are the inevitable concomitant in the current state of laser powder bed fusion (PBFLB/M) of AlSI10Mg components. Various pore characteristics, such as pore size and pore shape, influence the quality and affect the intended functionality of the component. Today, the experimental effort to find the appropriate process parameters for additive manufacturing (AM) results in high costs and long time-to-market. Pore formation is highly dependent on the applied process parameters. Consequently, pores can also be seen as an individual process fingerprint. Computed tomography is a commonly used measurement tool for AM components and can be used to comprehensively investigate process-induced defects. Furthermore, X-ray data allows an accurate categorisation of pores and provides a large amount of labelled data for supervised learning applications. The applied classification method classifies the pores into six classes (A-F) according to their shape and size. A total number of 3,066,249 pores detected in cylindrical samples were categorised and used for machine learning. The purpose of this work is to demonstrate an approach for predicting AM process parameters depending on the resulting pore distribution using supervised learning methods. The result is an expandable machine learning model based on an artificial neural network.     «

Pores are the inevitable concomitant in the current state of laser powder bed fusion (PBFLB/M) of AlSI10Mg components. Various pore characteristics, such as pore size and pore shape, influence the quality and affect the intended functionality of the component. Today, the experimental effort to find the appropriate process parameters for additive manufacturing (AM) results in high costs and long time-to-market. Pore formation is highly dependent on the applied process parameters. Consequently, po...     »