The structural, mechanical and tribological properties of ZA-27/SiC nanocomposites were investigated at micro/nanoscale. The nanocomposites with different volume fractions of nano-sized SiC particles were produced using the compocasting technique. The microstructure of nanocomposites was characterized with formation of SiC nano agglomerates, which were relatively uniformly distributed. The increase in SiC content contributed to the uniformity of their distribution. Also, the phenomenon of particle segregation in the form of particle-rich clusters, as well as particle-porosity clusters, was identified. The density level of composites decreased with the increase of the SiC content. The porosity followed a reverse trend. The tendency for formation of local particle-porosity clusters was the highest in ZA-27/1% SiC nanocomposite, causing the highest level of porosity. Increasing percentage of SiC content was followed by the increase in micro/nanohardness of the composites. The results of micro/nanoscale tribotests revealed that the reinforcing with SiC nanoparticles significantly improved wear and friction behavior of ZA-27 matrix alloy. The rate of improvement increased with the increase of SiC nanoparticle content, load, and sliding speed. The highest degree of changes corresponded to the change of the SiC nanoparticle content from 0 to 1 wt%. The further decrease of wear with SiC content (from 1 to 5 wt%) was almost linear. The different tribological behavior of tested ZA-27 matrix and ZA-27/SiC nanocomposites was influenced by differences of intensity of adhesion resulted in transferred layers of matrix material onto worn surfaces of Al₂O₃ ball counterpart. The intensity of adhesion significantly decreased with the increase of SiC nanoparticle content.

The tribological properties of dental materials, such as wear and friction, are crucial for ensuring their long-term reliability and performance. Traditional experimental approaches, while accurate, are often resource intensive and time consuming, prompting a need for efficient computational methods. This study explores the application of artificial neural networks (ANNs) to predict the tribological behavior of dental ceramic lithium disilicate (IPS e.max Cad). A genetic algorithm (GA) was used to optimize the ANN’s hyperparameters, improving its ability to model complex, nonlinear relationships between input variables, including normal load and velocity and output properties such as wear rate and friction coefficients. By integrating experimental data with an ANN, this study identifies key factors influencing tribological performance, reducing the dependency on extensive experimental testing. The results demonstrate that the optimized ANN model accurately predicts tribological behavior, offering a robust framework for material optimization. These findings emphasize the potential of combining ANNs and GAs to enhance the understanding and design of dental materials, accelerating innovation while addressing the challenges of traditional evaluation methods. This research underscores the transformative role of advanced computational approaches in tribology and material science.

The problem of measuring static friction at high temperatures and low contact pressures is theoretically analyzed, as well as the instruments for the accurate determination of the coefficient of stati

The application of CAD/CAM technologies in modern production has revolutionized manufacturing processes, leading to significant improvements in precision, efficiency, and flexibility. These technologies enable the design and manufacturing of complex geometries with high accuracy, reducing errors and material waste. CAD/CAM integration streamlines workflows, enhances productivity, and facilitates rapid prototyping, accelerating the time-to-market for new products. Additionally, it supports customization and scalability in production, allowing for cost-effective small-batch and large-scale manufacturing. Without a 3D model of the product, it is not possible to use the advantages of applying advanced CAD/CAM technologies. Recognizing 3D models from engineering drawings is essential for modern production, especially for outsourcing companies in fluctuating market conditions, where the production process is organized with 2D workshop drawings on paper. This paper proposes a novel methodology for reconstructing 3D models from 2D engineering drawings, specifically those in DXF file format, leveraging a genetic algorithm. A core component of this approach is the representation of the 2D drawing as a symmetric adjacency matrix. This matrix serves as the foundational data structure for the genetic algorithm, enabling the evolutionary process to effectively optimize the 3D reconstruction. The experimental evaluation, conducted on multiple engineering drawing test cases (including both polyhedral and cylindrical geometries), demonstrated consistent convergence of the proposed GA-based method toward topologically valid and geometrically accurate 3D wireframe models. The approach achieved successful reconstruction in all cases, with fitness scores ranging from 1.1 to 112.2 depending on model complexity, and average execution times from 2 to 100 s. These results confirm the method’s robustness, scalability, and applicability in real-world CAD environments, while establishing a new direction for topology-driven 3D reconstruction using evolutionary computation.