Experimental and machine-learning analysis of the compressive strength of fused filament fabricated MWCNTs reinforced PLA using SHAP,Taguchi,and response surface methodology

This study investigates the compressive behaviour of fused filament fabricated (FFF) acid-functionalized multi-walled carbon nanotube (MWCNT)-reinforced polylactic acid (PLA) composites through integrated experimental, statistical, and machine-learning (ML) approaches. PLA–MWCNT composite filaments containing 10 wt% MWCNTs were fabricated through melt compounding and re-extrusion, followed by FFF printing using varying raster build orientation (RBO), raster bead height (RBH), hot-end temperature (HET), and raster build pattern (RBP). A full-factorial experimental design comprising 54 parameter combinations was employed to evaluate compressive performance. The results identified RBO as the dominant process parameter governing compressive behaviour, followed by RBP and RBH. The maximum compressive strength of 121.89 MPa was achieved at 225°C HET, 0° RBO, 0.10 mm RBH, and honeycomb RBP. The improved compressive behaviour at lower RBH and 0° RBO was associated with enhanced interlayer load transfer and reduced interface-dominated failure. Honeycomb structures exhibited superior compressive stability relative to gyroid structures due to improved load-transfer continuity and structural stiffness. Comparative SEM, DSC, TGA, and FTIR characterization of neat PLA and PLA–MWCNT composites was performed to strengthen the structure–property interpretation of the material system. SEM analysis revealed relatively distributed MWCNT-rich regions without severe large-scale agglomeration. DSC analysis indicated modified crystallization behaviour after MWCNT incorporation, while TGA demonstrated improved thermal stability with increased onset degradation temperature from 320°C to 338.03°C. FTIR analysis confirmed the characteristic functional groups of PLA without significant alteration of the primary chemical structure after MWCNT incorporation. Among the developed ML models, random forest regression achieved the highest prediction accuracy with R² = 0.9686 and RMSE = 2.59 MPa, while SHAP analysis confirmed RBO as the most influential parameter affecting compressive performance. The integrated DOE–RSM–ML–SHAP approach provides an interpretable experimental–ML framework for prediction and analysis of compressive behaviour within the investigated parameter space for lightweight and structural applications.