Tribological behavior of PLA reinforced with boron nitride nanoparticles using Taguchi and machine learning approaches

• Taguchi L9 orthogonal array is used to analyze the effects of load, speed, sliding distance, and BNNP content. • Higher BNNP concentrations enhance wear resistance, significantly reducing wear rate. • COF analysis identifies applied load as the most influential factor, with BNNP content also playing a key role. • Machine learning analysis shows that random forest regression (MSE: 0.0002, 0.35 %) outperforms linear regression (MSE: 0.0006, 1.5 %). • Optical microscopy shows that the 0.04 PLA/BNNP composite exhibits reduced wear features, indicating improved durability. This research investigates the tribological characteristics of Polylactic Acid (PLA) reinforced with Boron Nitride Nanoparticles (BNNP). The study employs the Taguchi approach, examining the effects of applied load (20–60 N), speed (100–300 RPM), sliding distance (500–1500 m), and BNNP content (0–0.04) on tribological properties. Results indicate that the applied load significantly affects the wear rate, with higher filler concentrations enhancing wear resistance. The observed wear rates range from 0.011×10⁻³ g/m (0.04 PLA/BNNP at 20 N, 300 RPM, 1500 m) to 0.124×10⁻³ g/m (0.02 PLA/BNNP at 60 N, 100 RPM, 1500 m). ANOVA results reveal that applied load is the most influential factor, accounting for 53.01 %. Conversely, speed predominantly affects the coefficient of friction (COF), contributing 78.05 %. The COF values range from 0.069 (0.02 PLA/BNNP at 60 N, 100 RPM, 1500 m) to 0.319 (0.04 PLA/BNNP at 20 N, 300 RPM, 1500 m). A machine learning technique, i.e., Random Forest Regression, is employed to predict the tribological performance. The Relative Root Mean Square Error (RRSME) values decisively confirm that Random Forest Regression (23.86 % wear rate and 18.32 % COF) is more accurate than traditional linear regression approaches. Optical microscopy confirmed enhanced wear resistance for 0.04 PLA/BNNP composites. Integrating the Taguchi approach and machine learning effectively optimized the tribological performance of PLA-BNNP composites for diverse applications.