A machine learning method to predict printing time for the L-PBF process

The machine learning usage in the L-PBF process for metal powders helps to identify the optimal parameter combination. Machine learning can find non-linear correlations between the high number of variables of this production process. One of the obstacles to the widespread adoption of L-PBF in the industry, in addition to the high costs, is the long printing time required for a complex component. The possibility of an early evaluation of the 3D printing time could promote the overall diffusion of this production process in the industry. Correct time prediction can improve cost efficiency and production capacity, reducing energy consumption, environmental impacts, and lead time. This paper proposes a machine learning approach, such as Random Forest Regressor, to predict the printing time of a metal component starting from the STereo Lithography (.stl) CAD format for the L-PBF process. A case study is proposed to evaluate and demonstrate the approach, obtaining a high-level prediction accuracy.