Machine Learning-Assisted Synergistic Optimization of 3D Printing Parameters for Enhanced Mechanical Properties of PLA/Boron Nitride Nanocomposites

Additive manufacturing via fused deposition modeling (FDM) offers a versatile method for fabricating complex polymer parts; however, enhancing their mechanical properties remains a significant challenge, particularly for biopolymers such as polylactic acid (PLA). PLA is widely used in 3D printing due to its biodegradability and ease of processing, but its relatively low mechanical strength and impact resistance limit its broader applications. This study explores the reinforcement of PLA with boron nitride nanoplatelets (BNNPs) to improve its mechanical properties. This study also aims to optimize key FDM process parameters, such as reinforcement content, nozzle temperature, printing speed, layer thickness, and sample orientation, using a Taguchi L27 design. Results show that the addition of 0.04 wt.% BNNP significantly improves the mechanical properties of PLA, enhancing tensile strength by 44.2%, Young’s modulus by 45.5%, and impact strength by over 500% compared to pure PLA. Statistical analysis (ANOVA) reveals that printing speed and nozzle temperature are the primary factors affecting tensile strength and Young’s modulus, while impact strength is primarily influenced by nozzle temperature and reinforcement content. Machine learning models, such as CatBoost and Gaussian process regression, predict mechanical properties with high accuracy (R² > 0.98), providing valuable insights for tailoring PLA/BNNP composites and optimizing FDM process parameters. This integrated approach presents a promising path for developing high-performance, sustainable nanocomposites for advanced additive manufacturing applications.