Comparative evaluation of parametric models of porosity in laser powder bed fusion

Porosity is a critical defect in laser powder bed fusion that limits the adoption of this technology. The variations in process parameters affect the level of porosity in additively manufactured parts. Due to the complex multiphysics of the laser powder bed fusion process, surrogate models can be used to predict the amount of porosity from the process parameters. Regression and machine learning approaches have been used for the porosity prediction. However, these models are developed for certain materials. This study compares different surrogate models for correlating the amount of porosity and the process parameters in combination with proposed dimensionless numbers that are dependent to both the process parameters and powder material properties. Regression, support vector machine, and neural networks models are trained using lack of fusion porosity synthetic data for three different materials. The results show that the support vector machine and the multi-layer neural network models that use process parameters and the dimensionless numbers as the independent variables can effectively predict the amount of porosity regardless of materials.