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.

The main target of this study is to evaluate the impact of hybrid reinforcement using Al₂O₃ nanoparticles and graphite on the epoxy nanocomposites’ mechanical and tribological properties. Various weight fractions of the reinforcement materials, ranging from 0 to 0.5 wt.%, were incorporated into the epoxy. The aim is to enhance the characteristics and durability of the polymers for potential utilization in different mechanical applications. The addition of hybrid additives consisting of Al₂O₃ nanoparticles and graphite to the epoxy resin had a noticeable effect on the performance of the epoxy nanocomposites. The incorporation of these additives resulted in increased elasticity, strength, toughness, ductility, and hardness as the concentration of reinforcement increased. The enhancement in the stiffness, mechanical strength, toughness and ductility reached 33.9%, 25.97%, 25.3% and 16.7%, respectively. Furthermore, the frictional tests demonstrated a notable decrease in both the coefficient of friction and wear with the rise of the additives’ weight fraction. This improvement in the structural integrity of the epoxy nanocomposites led to enhanced mechanical properties and wear resistance. The SEM was utilized to assess the surfaces of tested samples and provide insights into the wear mechanism.

Hip joint fractures are a serious and prevalent health concern, especially among the elderly. The forces that the lower limbs must withstand are quite strong and can exceed many times the person body weight, especially in the femur. The selection of materials for joint replacements depends heavily on mechanical and tribological properties like strength, hardness, and wear rate. HDPE has been effective as an external prosthetic foot, but its mechanical and tribological constraints have made it difficult to employ as a part of an artificial implant. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) models are utilized to forecast HDPE nanocomposites mechanical and tribological characteristics. The model incorporates various weight fractions of graphene and relies on data obtained from experimental tests. These nanocomposites are prepared with various compositions, and their structure and morphology are evaluated using physiochemical characterization. Compared to pure HDPE, adding graphene enhanced the hardness, strength, and elastic modulus of the HDPE. The nanocomposite with a composition of 2 wt.% graphene showed better mechanical properties compared with pure HDPE. In addition, a finite element model is created to simulate the hip joint and assess the HDPE nanocomposite load-bearing capacity under actual loading. The outcomes of the simulation analysis are consistent with the experiment findings. In addition, in comparison to pure HDPE, the nanocomposite containing 0.5 wt.% graphene showed better tribological performances. Finally, the microscopic examination demonstrated how the additives weight fraction affects the HDPE nanocomposites wear mechanism. Eventually, the ANFIS models proved its ability to predict the tribo-mechanical properties of HDPE nanocomposite with an error that didn’t exceed 3%.

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.

Prosthetic feet are essential aids for individuals with disabilities, enabling them to stand and walk. However, current prosthetic foot designs often fall short in terms of material properties, potentially compromising safety on various surfaces. This study examines the impact of different rubber types, specifically their hardness, on the friction between prosthetic feet and flooring materials. We evaluated the static friction coefficients of five rubber types on four different floor surfaces under varying normal loads, utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Results demonstrate that rubber hardness and normal load significantly influence the friction coefficient. A decrease in rubber hardness increases the friction coefficient, thereby reducing slippage. Notably, a reduction in rubber hardness enhanced the friction coefficient by up to 62%, while an increase in normal load improved it by 35%. Additionally, dynamic friction coefficients were assessed through robotic locomotion experiments on different flooring surfaces using the various rubber materials. These findings provide valuable insights for enhancing the safety and performance of prosthetic feet.