Biomedical applications are crucial in rehabilitation medicine, assisting individuals with disabilities. Nevertheless, materials failure can sometimes result in inconvenience for users. Polylactic Acid (PLA) is a popular 3D-printed material that offers design flexibility. However, it is limited in use because its mechanical properties are inadequate. Thus, this study introduces an artificial intelligence model that utilizes ANFIS to estimate the mechanical properties of PLA composites. The model was developed based on an actual data set collected from experiments. The experimental results were obtained by preparing samples of PLA green composites with different weight fractions of date pits, which were then annealed for varying durations to remove residual stresses resulting from 3D printing. The mechanical characteristics of the produced PLA composite specimens were measured experimentally, while the ANSYS model was established to identify the composites' load-carrying capacity. The results showed that ANFIS models are exceptionally robust and compatible and possess good predictive capabilities for estimating the hardness, strength, and Young's modulus of the 3D-printed PLA composites. The model results and experimental outcomes were nearly identical.
Keywords: 3D printing; ANFIS; PLA green composite; artificial intelligent; rehabilitation medicine.