Prediction of Mechanical Properties for Carbon fiber/PLA Composite Lattice Structures Using Mathematical and ANFIS Models

This study investigates the influence of design, relative density (RD), and carbon fiber (CF) incorporation parameters on mechanical characteristics, including compressive modulus (E), strength, and specific energy absorption (SEA) of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS lattice structures were evaluated under uniaxial compression testing based on the design of experiments (DOE) approach, namely, full factorial design. Prediction modeling was conducted and compared using mathematical and intelligent modeling, namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed the 3D printing imperfections (e.g., RD variations) to be taken into account by considering the actual RDs instead of the designed ones, as in the case of mathematical modeling. In this regard, this was the first time the ANFIS modeling utilized the actual RDs. The desirability approach was applied for multi-objective optimization. The mechanical properties were found to be significantly influenced by cell type, cell size, CF incorporation, and RD, as well as their combination. The findings demonstrated a variation in the E (0.144 GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models outperformed mathematical models in predicting all mechanical characteristics, including E, strength, and SEA. For instance, the maximum absolute percent deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical prediction. The accuracy of mathematical predictions is highly influenced by the degree of RD deviation: a higher deviation in RD indicates a lower accuracy of predictions. The findings of this study provide a prior prediction of the mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better understanding of their potential and limitations.