Accelerated process parameter selection of polymer-based selective laser sintering via hybrid physics-informed neural network and finite element surrogate modelling

The state of the melt region as well as the temperature field are critical indicators reflecting the stability of the process and subsequent product quality in selective laser sintering (SLS). The present study compares various simulation models for analyzing melt pool morphologies, specifically considering their complex transient evolution. While thermal fluid dynamic simulations offer comprehensive insights into melt regions, their inherent high computational time demand is a drawback. In SLS, the polymer's high viscosity and low conductivity limit liquid flow, thereby promoting a slow evolution of the melt region formation. Based on this observation, utilizing low-complexity pure heat conduction simulation can be adequate for describing melt region morphologies as compared to the more complex thermal fluid dynamic simulations. In the present work, we propose such a purely conduction based finite element (FE) model and use it in combination with an AI-powered partial differential equation (PDE) solver based on a parametric physics-informed neural network (PINN). We specifically conduct the simulations for the sintering process, where large thermal gradients are present, with the parametric PINN based model, whereas we employ the finite element method (FEM) for the cooling phase in which gradients and cooling rates are several orders lower, thus enabling the prediction of sintering temperature and melt region morphology under various configurations. The combined hybrid model demonstrates less than 7% deviation in temperatures and less than 1% in melt pool sizes as compared to the pure FEM-based models, with faster computational times of 0.7 s for sintering and 20 minutes for cooling. Moreover, the hybrid model is utilized for multi-track simulation with parametric variations with the purpose of optimizing the manufacturing process. Our model provides an approach to determine the most suitable combinations of settings that enhance manufacturing speed while preventing issues such as lack of fusion and material degradation.