A cybermanufacturing and AI framework for laser powder bed fusion (LPBF) additive manufacturing process

Abstract

In Laser Powder Bed Fusion (LPBF) along, more than 50 process parameters are known to affect print quality. The current state-of-the-art practice in process control only considers a small fraction of them – mainly on laser power and scanning speed affecting temperature gradient and geometry of a melting pool. This letter proposes a system-wide platform involving various machine learning principles and leveraging production data stored in the cloud. The proposed framework aims to identify process parameters that may affect print quality so that a viable process control strategy can be formulated.