Abstract: This paper presents a multifunctional health monitoring system based on the STM32F103C8T6 microcontroller, capable of real-time tracking of multiple vital signs, including body temperature, heart rate, blood oxygen saturation, blood pressure, and body posture. Equipped with a Wi-Fi module, the system enables real-time remote monitoring of the user's health status and triggers an alarm upon detection of abnormal health indicators. By integrating multiple sensors in a coordinated manner, the system provides reliable health monitoring and enhances personal safety Read More

Abstract: Bearings are essential components in mechanical systems, with their performance being a key factor influencing the reliability and service life of machinery. Bearing test rigs, as critical equipment for evaluating bearing performance, simulate diverse operating conditions to investigate failure mechanisms and performance characteristics under practical applications. These evaluations offer crucial insights for optimizing bearing design and operational parameters. Specialized working environments, such as heavy-load conditions, deep-sea operations, space applications, and extreme high/low temperatures, impose stringent demands on bearing performance. Consequently, performance assessment and lifespan evaluation of bearings in these conditions have become focal points in the development of advanced bearing test rigs. This paper reviews the research progress of bearing test rigs under heavy load conditions, deep-sea environments, space environments, and high/low-temperature environments. It analyzes the characteristics of bearing test rigs under different environmental simulation technologies and provides an outlook on the research trends in this field, offering a solid foundation for technological advancements in related areas. Read More

Abstract: To study fracture mechanics in elastomeric materials, digital image correlation (DIC) was used to characterize the deformation field near the crack tip in industrial styrene-butadiene rubber. The sectoral distribution and strain singularity near the crack tip were systematically examined under different loading levels. Experimental results indicated that the crack-tip region was divided into an Expansion Sector (EX) and two Shrinking Sectors (SH). The EX angle increased with the applied load and eventually converged to approximately . Moreover, the strain field near the crack tip demonstrated singular behavior, characterized by three distinct layers: a nonlinear singular layer, an exponential singular layer, and a linear elastic layer. The singularity exponent of the exponential layer remained constant under increasing load, suggesting that the applied load amplified the strain magnitude without changing the intrinsic singularity behavior. These results validate the applicability of the fan-shaped sectoring theory for analyzing large-deformation fracture in styrene-butadiene rubber. Read More

Abstract: Casting defect detection is a crucial link in industrial production, directly affecting product quality and safety. Traditional manual inspection methods, due to their low efficiency, strong subjectivity, and high missed detection rates, can no longer meet the demands of modern intelligent manufacturing. In recent years, the breakthrough progress of deep learning technology in the field of image recognition has provided new solutions for automated casting defect detection. This paper systematically reviews automatic casting defect recognition methods based on deep learning, focusing on aspects such as algorithm improvements, practical applications, and future development directions. It discusses in detail the optimization strategies of object detection algorithms (such as the YOLO series, Mask R-CNN) and semantic segmentation algorithms (such as U-Net, Retina Net), and summarizes the latest achievements in industrial inspection system development. Finally, it prospects future research directions including self-supervised learning, unsupervised learning, and real-time quantitative analysis, aiming to promote the development of casting inspection technology towards intelligence and automation. Read More

Abstract: Dexterous hands are pivotal for enabling advanced robotic manipulation. This review synthesizes recent advances in structural design, actuation, sensing, and control algorithms. Structurally, trends shift from serial/parallel mechanisms toward hybrid rigid-soft biomimetic designs. Actuation leverages electromagnetic, fluidic, and smart-material systems for improved precision and compliance. Multi-modal sensing—integrating force, position, tactile, and vision data—enhances environmental awareness. Control algorithms evolve from classical PID to intelligent strategies like neural networks and reinforcement learning for adaptive manipulation. Persistent challenges include flexibility-stability trade-offs, sensing latency, and high costs. Future directions emphasize AI integration, lightweight designs, and improved reliability for complex tasks in industrial and biomedical applications. Read More

Abstract: An improved algorithm for generating digital speckle patterns is proposed. This method introduces a minimum spacing constraint and a multi-shape random distribution strategy to overcome two common limitations of conventional approaches: limited morphological diversity and undesirable overlap at high densities. A comprehensive evaluation was performed using four key parameters: speckle size, mean intensity gradient, systematic error, and random error. The digitally designed speckle patterns were transferred onto a CNTs/PDMS composite via a non-destructive technique. This approach successfully enabled the measurement of deformation fields in the crack tip region under large-strain conditions. Based on the experimental results, the fan-shaped zoning characteristics of the deformation field near the crack tip were analyzed. The experimental results validate that the speckle patterns produced by the proposed algorithm are high-contrast and well-distributed, demonstrating significant potential for applications in fracture mechanics studies of flexible composites. Read More

Abstract: The hot rolling process of a conical cylindrical part made of 2A14 aluminum alloy was investigated using finite element simulation with Simufact-Forming. A numerical rolling model was established, and the forming behavior of the component at different deformation stages was systematically analyzed. The simulation results indicate that the proposed approach effectively reflects the deformation characteristics of conical cylinders and provides practical guidance for the rolling of non-standard ring components. This study demonstrates that finite element simulation not only contributes to improving the forming quality of ring parts and reducing production and development costs but also enhances industrial competitiveness. Furthermore, the findings offer valuable insights for the practical application of radial–axial rolling in complex ring components. Read More

Abstract: As core power equipment, the stable operation of centrifugal compressors is critical to industrial systems. Stall and surge represent severe unstable operating conditions that threaten compressor safety, potentially leading to equipment damage and production interruptions. To address the limitations of traditional control strategies in handling system nonlinearity, time-varying characteristics, and model uncertainty, this paper proposes an intelligent control method based on the Grasshopper Optimization Algorithm (GOA). The study first establishes a nonlinear model that accurately reflects the compressor's dynamic characteristics. Subsequently, advanced controllers (such as fuzzy PID and model predictive controllers) are designed with the objectives of expanding stability margin and suppressing pressure pulsations. To tackle the challenge of tuning critical controller parameters, the GOA algorithm is employed for global optimization. Leveraging its robust optimization capabilities and convergence speed, it adaptively obtains optimal parameter combinations to enhance the system's dynamic response performance and disturbance rejection capability. Simulation and experimental results demonstrate that compared to conventional PID and empirical tuning methods, the GOA-optimized control strategy more effectively delays the surge onset boundary, rapidly suppresses pressure oscillations under disturbance conditions, and significantly enhances the operational stability and control quality of centrifugal compressors. This provides a novel solution for the safe, efficient, and intelligent operation of compressors. Read More

Abstract: Power skiving is a highly efficient and high-precision gear machining method. During the power skiving process, the cutting force and cutting temperature exerted on the tool significantly influence its service life. Improper selection of process parameters can easily lead to rapid wear and failure of the skiving tool. Currently, the characteristics of cutting forces and thermal behavior in such machining processes remain unclear, lacking theoretical guidance for tool design and process parameter optimization. To address these issues, this study designed and developed a three-dimensional model of a curved rake face skiving tool. A finite element-based simulation model of the power skiving process was established to calculate the cutting force and cutting temperature data. The influence of key process parameters on the cutting performance was analyzed. A full factorial experimental approach was adopted to optimize the power skiving process parameters based on the mean and peak values of cutting force, as well as the mean value of cutting temperature. Through comprehensive analysis, the primary factors affecting the cutting force and cutting temperature in power skiving were identified. Read More