Predictive modeling of lattice structure design for 316L stainless steel using machine learning in the L-PBF process

Abstract

Lattice structures in additive manufacturing of 316L stainless steel have gained increasing attention due to their well-suited mechanical properties and lightweight characteristics. Infill structures such as honeycomb, lattice, and gyroid have shown promise in achieving desirable mechanical properties for various applications. However, the design process of these structures is complex and time-consuming. In this study, we propose a machine learning-based approach to optimize the design of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using laser powder bed fusion (L-PBF) technology under different loading conditions. A dataset of simulated lattice structures with varying geometries, wall thickness, distance, and angle using a computational model that simulates the mechanical behavior of infill structures under different loading conditions was generated. The dataset was then used to train a machine learning model to predict the mechanical properties of infill structures based on their design parameters. Using the trained machine learning model, we then performed a design exploration to identify the optimal infill structure geometry for a given set of mechanical requirements and loading conditions. Finally, we fabricated the optimized infill structures using L-PBF technology and conducted a series of mechanical tests to validate their performance under different loading conditions. Overall, our study demonstrates the potential of machine learning-based approaches for efficient and effective designing of honeycomb, lattice, and gyroid infill structures in 316L stainless steel fabricated using L-PBF technology under different loading conditions. Furthermore, this approach can be used for dynamic loading studies of infill structures.