Enhancing Finite Element Simulations of 3D-Printed PLA Using PINN: Validated Through Experimental Testing

The accuracy of finite element analysis (FEA) simulations for 3D-printed parts is often limited by assumptions regarding the material's mechanical properties, particularly when these properties are derived from conventional injection-molded specimens rather than directly measured from 3D-printed components. The acquisition of precise mechanical properties for FEA requires testing 3D-printed samples that exactly replicate printing parameters such as raster angle and infill density and layer thickness, which is a time-consuming and expensive procedure. The research presents an advanced technique that improves FEA simulation precision for PLA 3D-printed part tensile strength through Advanced Physics-Informed Neural Networks (PINN). Experimental tensile test data from various raster angles, infill densities, and layer thicknesses enable us to train a PINN for material property prediction which could differ substantially from traditional injection-molded PLA properties. The approach enables the prediction of tensile strength across various printing options while eliminating the requirement for additional physical tests. The research demonstrates that combining PINNs with standard FEA techniques leads to better simulation accuracy and represents a budget-friendly alternative to direct testing. The proposed method demonstrates how machine learning technology speeds up computational methods while addressing the precise modeling challenges of 3D-printed materials.