Linking process parameters with lack-of-fusion porosity for laser powder bed fusion metal additive manufacturing

Structural defects such as porosity have detrimental effects on additively manufactured parts which can be reduced by choosing optimal process conditions. In this work, the relationship between process parameters and lack-of-fusion (LOF) porosity has been studied for the laser powder bed fusion (L-PBF) process of the Ti-6Al-4V alloy (Ti64). A physics-based thermo-fluid model is used to predict LOF porosity in the multilayer multitrack PBF process. To effectively map the high-dimensional processing parameters with porosity, an active learning framework has been adopted for the optimal design of experiments. Furthermore, a customized neural network-based symbolic regression tool has been utilized to identify a mechanistic relationship between processing conditions and LOF porosity. Results indicate that combining the physics-based thermo-fluid model for PBF porosity prediction with active learning and symbolic regression can find an appropriate mechanistic relation-ship of LOF porosity that is predictive for a wide range of processing conditions. This mechanistic relationship was further tested for other metal AM materials systems (IN718, SS316L) through non-dimensional numbers. The presented workflow effectively explores the high-dimensional process design space for different additive manufacturing materials systems.

Automated summary

In this study, a physics-based thermo-fluid model is used to predict the lack-of-fusion (LOF) porosity in the laser powder bed fusion (L-PBF) process. An active learning framework and symbolic regression are adopted to find the mechanistic relationship between process parameters and LOF porosity. The results show that this relationship is predictive for a wide range of processing conditions and can effectively explore the high-dimensional process design space.