Analysis of Density of Laser Powder Bed Fusion Fabricated Part Using Decision Tree Algorithm

Abstract

Additive manufacturing (AM) enabled manufacturing industries to fabricate metallic components with complex shapes. However, the properties of additively manufactured parts need further improvements to compete with the performance of traditionally manufactured parts. Machine learning (ML) models provide an alternative to study the correlation between the process parameters–properties of the fabricated parts. In the present work, the ML approach has been applied to understand the effect of AM process parameters on the density of additively built parts. The decision tree model was developed for the laser powder bed fusion-processed parts based on the input parameters such as laser power, scan speed, hatching space, energy density, and build rate. The model was trained and tested with experimental data obtained from the relevant literature. The process parameters were optimized to achieve the desired density of the part. A good agreement was indicated between the predicted and experimental data. The study revealed the applicability and potential of the model to determine and predict the density of the additively manufactured parts.