Strain wave gears are widely used as reducers in robots, including collaborative and industrial robots. As a key component, they play a crucial role in determining overall robot performance. To enhance their effectiveness, various studies have focused on directly measuring the performance of assemblies or predicting the performance of individual components through analysis. However, there is a notable lack of research that experimentally measures and compares the physical properties of the circular spline, flexspline, and wave generator—the primary elements of strain wave gears. In this paper, we developed equipment to measure the radial stiffness of the flexspline, one of the key components, and validated its reliability through preliminary experiments. Furthermore, we measured and compared the radial stiffness of flexsplines produced by three different manufacturers. These findings are expected to provide valuable insights for improving the performance of strain wave gears and advancing robotics technology.

In the field of construction automation, significant research efforts continue to focus on replacing human labor; however, the varied and dynamic nature of construction sites still requires human intervention. The high task intensity in construction sites, particularly in lifting heavy materials, frequently results in musculoskeletal disorders among workers. To address this issue, this paper proposes a lifting device to replace manual material transportation through an opening between floors. The lift is designed with a gear-constrained double parallelogram mechanism to enable straight vertical movement. Moreover, a crank-rocker mechanism is incorporated to improve efficiency in repetitive tasks, reduce the required driving torque, and simplify control complexity. Additionally, this study introduces a passive gravity compensation mechanism that employs springs and cables, tailored to the lifting process, to enhance payload capacity and stabilize actuation. Through the integration of these mechanisms, the necessary motor capacity and control costs are significantly reduced. The effectiveness of the device is validated by actuation experiments with a fabricated prototype.

The purpose of this paper was to develop a simulation model for a 40 kW electric tractor using a powertrain based on dual motors and a planetary gear. To select motor capacity and reduction gear ratio based on the power flow for agricultural work, load data for various gear conditions were acquired and analyzed using a 42 kW engine tractor of similar capacity. Modeling was conducted using MATLAB/Simulink/Simscape. Load data acquired through actual field tests were applied as load conditions for the simulation. Simulation results confirmed that the power was transmitted through the planetary gear as the clutch and brake operated according to the work mode. The developed simulation model is expected to be used for electric tractor development.

In the field of optical engineering, the laser position control system has important role in many applications, such as measurement, communication, fabrication. Traditional methods to solve laser position control system often face the problems of insufficient generalization, such as configuration or singular solution. In this study we proposed a novel model- free reinforcement learning approach based Proximal Policy Optimization (PPO) for laser position control system. To control the position of laser, we develop an efficient representation of environmental inputs and outputs. Position error of Position Sensing Detector (PSD), and three kinds of distance parameters are applied our environmental parameters. To overcome the challenges associated with training in real worlds, we developed training environment in simulation. The simulation to evaluate performance of our approach, we perform several times of experiments in both simulated and real world system.

Chemically strengthened glass has recently gained attention for use in mobile device display covers due to its enhanced mechanical properties. However, cutting chemically strengthened glass poses challenges because of its high surface compressive stress, derived from the ion exchange between Na+ and K+ during the strengthening process. To address this, we propose an efficient method for cutting chemically strengthened glass by integrating electrochemical discharge (ECD) and grinding processes. The ECD process helps alleviate surface compressive stress through reverse ion exchange, while the grinding process helps mitigate compressive stress on the bottom surface without flipping the glass. Chemical composition analysis of the cross-section of glass cut along the line treated by the ECD process revealed that this method can induce reverse ion exchange on both the upper and bottom surfaces of chemically strengthened glass. Furthermore, nano-indentation hardness tests conducted on the cross-section demonstrated that the subsurface hardness could be reduced by the ECD process, indicating a relaxation of the surface compressive layers. It has also been proven that chemically strengthened glass can be successfully cut using this method, suggesting it offers a viable solution for efficient glass cutting.

Microfluidics allows for precise manipulation of small volumes of analytical solutions in diverse applications, including disease diagnostics, drug efficacy testing, chemical analysis, and water quality monitoring. Among these diverse applications, one of the most critical aspects is the precise and programmable control of flow within microfluidic control devices. However, microfluidic experiments that employ pressure control via a gas tank may encounter restricted mobility. To address these challenges, we developed an air pump feedback control system utilizing artificial intelligence image analysis and devised a method to enhance portability. In this paper, we utilized a commercially available portable pump to achieve the desired pressure and subsequently cease operation. In addressing the challenge of sustaining prolonged pressure, we implemented a strategy wherein the dimensions of the pressure vessel were modified, accompanied by iterative pump activations, thereby ensuring the sustained maintenance of pressure over time. The evaluation of the flow controller developed in this study involves conducting a comparative flow analysis with established pneumatic flow controllers. Furthermore, we employed artificial intelligence image analysis methods to automate the operation of iterative pumps. In conclusion, we anticipate that the developed portable microfluidic control device will lead to innovative advancements in modern technology and healthcare through its potential applications.

In continuous-process systems, failures of rolling-element bearings typically cause accidents, reduced productivity, and production-related financial losses. Therefore, predicting both the lifespan of rolling-element bearings and their replacement time is crucial for preventing machine system failures. Accordingly, numerous studies have reported various machine and deep learning classifiers for predicting the lifespan of bearings. However, these studies did not consider degradation trends of bearings. Thus, this study aimed to develop an algorithm to predict the lifespan of a bearing by considering its degradation trend. A vibration dataset of bearings was obtained at low and high speeds. Using a second-order curve-fitting model, various degradation patterns in the dataset were classified. Appropriate time-domain or frequency-domain feature variables applicable to the design of a classifier were determined according to classified patterns. In addition, the classifier was trained using multiple bidirectional long short-term memories. Finally, the performance of the developed classifier was verified experimentally.

A compositional library of Ag-Ti thin films was fabricated using combinatorial RF magnetron sputtering. The films exhibited a gradual compositional gradient across the substrate, ranging from Ag-rich to Ti-rich compositions. SEM analysis revealed a uniform thickness of approximately 150 nm for all films. The relationship between composition and properties was investigated, demonstrating that increasing Ag content led to decreased resistivity and increased density. These results can be attributed to the high electrical conductivity and density of Ag. To optimize SAW device performance, a balance between resistivity and density must be achieved. While Ag-rich films offer higher electrical conductivity, they may experience reduced inverse piezoelectric effects due to increased density. Conversely, Ag-poor films may have improved inverse piezoelectric effects but reduced electrical conductivity.

This study investigates the influence of operating diametral clearance on the performance of angular contact ball bearings (ACBBs). It examines critical factors affecting diametral clearance, including mounting conditions, external loads, temperature fluctuations, and rotational speeds. A novel model combining quasi-static and fit-up approaches is proposed to analyze the effects of operating diametral clearances on ACBB performance. This model incorporates key elements such as ball-race contact loads, interactions between the shaft and inner ring, interference fits between the housing and outer ring, centrifugal expansion of the rotating shaft and inner ring, and temperature-induced changes. Internal clearance variations are computed using the thick-ring theory. Simulations are conducted to predict ACBB characteristics under various fit-up conditions, including contact load distribution and stiffness, with results validated using commercial software. The study also explores the impact of various operating diametral clearances on ACBB performance under differing fitting conditions, external loads, and rotational speeds.

This paper proposes an algorithm to improve path planning and tracking performance for autonomous robots using a Four- Wheel Steering (4WS) system in constrained environments. Traditional Ackermann steering systems face limitations in narrow spaces, which the 4WS system aims to address. By extending the Hybrid A* algorithm to adapt to the unique characteristics of the 4WS system, and integrating it with Model Predictive Control, the study achieves efficient path planning and precise tracking in complex environments. A distinctive aspect of the proposed approach is its adaptive control strategy, dynamically switching between three modes—Normal driving, Pivot, and Parallel movement—based on the vehicle's motion state, thus enhancing both flexibility and efficiency. The algorithm's performance was validated through MATLAB simulations in a logistics warehouse setting, showing high path tracking accuracy in confined spaces. The study effectively demonstrates the feasibility of the proposed method in a simulated environment.