Data-driven machine learning modelling and sensitivity analysis of PLA–MoS 2 /SiC properties for sustainable 3D printing

Abstract

The demand for sustainable and high-performance materials in additive manufacturing has accelerated the development of reinforced biopolymer composites for fused filament fabrication (FFF). In this study, polylactic acid (PLA) was reinforced with molybdenum disulfide (MoS 2 ) and silicon carbide (SiC) powders to fabricate multifunctional composite filaments tailored for 3D printing applications. Experimental datasets were generated by systematically varying filler composition, extrusion temperature, and screw speed during filament production. The experimental testing results of PLA–MoS 2 /SiC filaments were obtained through tensile testing. The porosity trends are consistent with the ANN-based predictions and sensitivity analysis. Lower porosity correlates with higher predicted strength and composition plays a dominant role in reducing void formation. To further enhance predictive capability and process optimization, an artificial neural network (ANN) model was developed for estimating key mechanical properties based on the input processing parameters. The ANN model demonstrated effective optimization yielding high correlation coefficients of 0.95 for training, 0.98 for validation, 0.98 for testing, and 0.96 overall. The optimal validation performance was achieved with a mean squared error (MSE) of 5855.29 at the three epoch, with the training process completing in just 3 epochs. These outcomes confirm that the ANN model exhibits strong stability and reliability in predicting the process parameters. Overall, this work illustrates the potential of ANN-based data-driven modelling to accelerate the design of sustainable PLA-based composite filaments and supports their suitability for next-generation 3D printing applications.