Optimizing 3D printed diamond lattice structure and investigating the influence of process parameters on their mechanical integrity using nature-inspired machine learning algorithms

Abstract

This research investigates the effect of process parameters on the mechanical integrity of 3D-printed Polylactic Acid (PLA+) lattice structures, with a focus on diamond lattice configurations. Adhering to ASTM D695 standards, the study employs compression testing to analyze the impact of various factors, including layer height, infill pattern, cell size, and infill density. The printing process utilizes the Ultimaker Cura Fused Deposition Modelling method, ensuring accuracy and uniformity in sample production. Mechanical properties were assessed using a Universal Testing Machine, highlighting the influence of the studied parameters on compressive strength and specific energy absorption (SEA). Innovatively, this study integrates nature-inspired machine-learning algorithms for hyperparameter tuning. Particle Swarm Optimization (PSO) is employed to optimize the settings of Random Forest and XGBoost models, enhancing their predictive capabilities. Results indicate that XGBoost, fine-tuned with PSO, slightly outperforms its counterpart in predicting key mechanical properties. Furthermore, feature significance assessments reveal cell size as a critical factor for compressive strength. This research not only provides a comprehensive analysis of mechanical properties in 3D-printed structures but also introduces a novel approach to applying nature-inspired algorithms for machine learning optimization in additive manufacturing.