Omar Salah

3D-printed polymers have been applied in various fields. Machine learning (ML) has revolutionized material science by providing powerful tools for modeling complex properties. This study investigates the comparative performance of interpretable and black-box ML techniques in modeling the mechanical and tribological properties of 3D-printed PLA composites blended with varying amounts of date pit particles. While neural networks (NNs) can model complex input–output relationships with high accuracy, they function as black-box models, limiting the understanding of their predictions. To address this limitation, we propose Sequential-thresholded Least-squares Sparse Regression (SLSSR), an interpretable modeling approach. SLSSR constructs models using no more than five basis functions and achieves a mean absolute error of less than 2%, ensuring both accuracy and model transparency. Moreover, SLSSR outperforms NN, delivering better accuracy and a reduced standard deviation in predictions, particularly with smaller training datasets. These results demonstrate SLSSR’s effectiveness and potential as a reliable tool for material science applications, especially in data-limited scenarios.

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.

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.

Finding the optimum path for mobile robots is now an essential task as lots of autonomous mobile robots are widely used in factories, hospitals, farms, etc. Many path planning algorithms have been developed to finding the optimum path with the minimum processing time. The vertical cell decomposition algorithm (VCD) is one of the popular path planning algorithms. It is able to find a path in a very short time. In this paper, we present a new algorithm, called the Radial cell decomposition (RCD) algorithm, which can generate shorter paths and a slightly faster than VCD algorithm. Furthermore, the VCD algorithm cannot be applied directly to obstacles in special cases, like two vertices have the same x-coordinate; on the other hand, the RCD algorithm can be applied to these special cases directly. In addition to that, the RCD algorithm is very suitable for corridor environments, unlike the VCD algorithm. In this paper, the RCD algorithm is described and tested for both cluttered and corridor environments. Furthermore, Two different algorithms A*, and Vertical cell decomposition are compared to the RCD algorithm. Simulation results confirm the effectiveness of the RCD algorithm in terms of path length and processing time.

Robotic exploration of wide terrains, such as agricultural fields, could be challenging while considering the limited robot’s capabilities in terms of sensing and power. Thus, in this paper, we proposed OGPR*, an Obstacle Guided Path Refinement algorithm for quickly planning collision-free paths utilizing the obstacles existing in the environment. To tackle the issue of exploring wide terrains, a supervisory-based collaboration between the quadcopter and a mobile robot is proposed. The quadcopter is responsible for streaming subsequently live two-dimensional images for the environment under discussion while planning safe paths for the ground the mobile robot is planning safe paths to manoeuvre. Numerical simulations proved the significant performance of the proposed OGBR* algorithm when compared to the state of the art algorithms exist in the literature.