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Tribological performance is a critical aspect of materials used in biomedical applications, as it can directly impact the comfort and functionality of devices for individuals with disabilities. Polylactic Acid (PLA) is a widely used 3D-printed material in this field, but its mechanical and tribological properties can be limiting. This study focuses on the development of an artificial intelligence model using ANFIS to predict the wear volume of PLA composites under various conditions. The model was built on data gathered from tribological experiments involving PLA green composites with different weight fractions of date particles. These samples were annealed for different durations to eliminate residual stresses from 3D printing and then subjected to tribological tests under varying normal loads and sliding distances. Mechanical properties and finite element models were also analyzed to better understand the tribological results and evaluate the load-carrying capacity of the PLA composites. The ANFIS model demonstrated excellent compatibility and robustness in predicting wear volume, with an average percentage error of less than 0.01% compared to experimental results. This study highlights the potential of heat-treated PLA green composites for improved tribological performance in biomedical applications.

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.