Many commercial optical glasses exhibit thermorheologically simple behavior, which allows the application of the time-temperature superposition principle within the temperature range from the glass transition temperature (T_g) to the deformation point (A_t). However, the molding temperature is set above A_t in cases involving significant deformation during glass molding press, such as with large-aperture concave meniscus lenses. Additionally, stress relaxation is observed in optical glasses even at temperatures below T_g. Nevertheless, the applicability of the time-temperature superposition principle to optical glasses within the temperature range below T_g and above A_t has not been thoroughly investigated. Therefore, in this study, uniaxial compression creep tests were conducted over a wide temperature range from approximately T_g - 20 °C to A_t + 50 °C using three types of commercial optical glasses to evaluate their thermo-viscoelastic properties. The results revealed that when the creep functions at each test temperature were shifted along a logarithmic time axis, they formed a smooth single curve (master curve), indicating the applicability of the time-temperature superposition principle. Moreover, while the shift factors for all glasses exhibited Arrhenius behavior within the temperature range of T_g to A_t, in two types of glasses, they exhibited a curved change below T_g and above A_t. Therefore, applying the Williams–Landel–Ferry (WLF) equation to approximate these shift factors revealed that they could be well approximated across the entire test temperature range. However, when applying the WLF equation to optical glass, limiting the temperature to a range higher than T_g - 30 °C was necessary. To express the shift factor over a wide temperature range of several hundred degree Celsius, from room temperature to molding temperature, it was practical to use the multi-linear Narayanaswamy equation, in which the apparent activation energies vary near A_t.

For sample return missions from deep space, a thin aeroshell capsule was proposed aimed at mitigating the aerodynamic heating during its reentry into the earth's atmosphere. The functional requirements of the capsule mandate that it aerodynamically decelerate sufficiently and land in the appropriate attitude without a parachute. To evaluate its motion attitude during transonic conditions in a wind tunnel, we employed the 1-degree of freedom (1-DOF) oscillation method. Furthermore, to predict its motion during an actual flight based on the wind tunnel test results, we examined the impact of the moment of inertia (MOI) on the motion. In all the MOI cases investigated, the limit cycle oscillation (LCO) was observed with a constant amplitude of angle. The variations in the MOI resulted in differences in the amplitude of the angle and time to reach the LCO. This study focused on the growth phase until the LCO and evaluated the characteristics on the phase plane. Consequently, a gradual approach to acceleration attributed to the static moment was observed in the growth history. A new modeling approach, centered on the nullcline during the growth phase, was adopted. Here, an approximation using a third-order function of angular velocity was applied to reproduce the nullcline obtained from the experiment. Upon non-dimensionalization of this model equation using the motion period, it was confirmed that the non-dimensional parameters were affected by the motion period, while this model focused solely on the growth phase. Furthermore, upon parameter tuning to replicate the amplitude of the angle and time to reach the LCO, and using these parameters, we predicted the impact of the MOI. The predicted results qualitatively reproduced larger MOI leading to larger amplitudes of angle and longer times to reach the LCO, which was consistent with the experimental results.

A model-based controller is effective for highly accurate and fast control of a robot. The dynamics model is derived from its equations of motion, and the minimum set of the dynamics parameters are experimentally identified. However, the experimental data includes the influence of both noise and un-modeled dynamics, the obtained model will be one of approximated solutions. From these considerations, the approximated model has to well represent the robot dynamics around the reference motion, and for high accuracy, new data needs to be added every time an experiment is conducted, which causes a lot of computation. The authors have proposed stochastic identification method to obtain suitable parameters for control system design. However, in this method, because of computational complexity of weighted least square mean, it is difficult to add new motion data. In this paper, we propose a sequentially updating parameter identification method based on statistical properties of the conventional method. By synthesizing the covariance of the parameter error, the nominal parameters are updated. The proposed method is performed to experiments using a planar 3-link manipulator, and its effectiveness is evaluated.

This work investigates the availability of the baseline approach proposed in the previous researches, and expands the applicability of the method for noise and vibration optimization. Based on the s-plane extension theory, baseline was formed by introducing a virtual damping in the transfer function calculation formula. Sensitivity analysis based on baseline was conducted to predict the changing tendency by structure modification in the target frequency range. The validity of the proposed method is examined through numerical simulation with two finite element (FE) models. A simple hollow rectangular parallelepiped model was constructed for confirming the effect of baseline sensitivity analysis as a structural vibration reduction treatment. And a vehicle frame-panel structure was constructed for confirming the proposed method as a noise optimization example. In view of the model in discussion, the sum of squares sensitivity or root-mean-square sensitivity was calculated to determine the mass attachment location. The conventional sensitivity values using FRFs without virtual damping(FRF method) were also calculated for comparing with baseline sensitivity results. The FE models with mass attachment based on sensitivity analysis results were calculated for confirming the vibration reduction effects. The result shows that FRF method has a greater decrease at some single peak in the target frequency range, while the baseline method has a better reduction performance at multiple peaks in the target frequency range. The selection strategies of the two methods for the mass attachment location and the change of mode shape after mass attachment were discussed. The baseline sensitivity method presented in this study provides a feasible approach for noise and vibration performance improvement in the medium frequency band.

Skilled technicians primarily use manual tools to maintain the geometry and surface conditions of railcar wheels. To ensure efficient wheel maintenance, the development of an automated inspection system is required. Herein, an inspection method for railcar wheels is proposed based on the assessment of wheel tread dimensions and surface conditions and investigation results of the method are presented. The parameters of wheel tread dimensions and surface conditions are considered because they are crucial for railway operation safety and manually inspecting them requires a large workforce. To improve the efficiency of the system, a combined inspection method through laser measurement and machine vision is used, both the techniques compensate for each other’s shortcomings. The experiments are conducted using laser sensors and machine vision techniques, including defect detection using AI model based on YOLOv5, as well as image data captured via a digital video camera to obtain high-accuracy automatic measurements and determine the feasibility of such measurements. Additionally, the measurement accuracy of the proposed method is verified based on experiments on sample wheel having wheel tread surface defects. In this paper, sample wheel including various wheel tread surface defects is used.

This study modeled the behavior of pedestrians who avoids moving obstacles, especially other pedestrians or bicycles are approaching, which are common in daily life. In our previous research, we modeled the behavior of a pedestrian when they avoided a static obstacle, such as a parked car, parked bicycle, or poles on a roadside. However, pedestrian motion might be affected by movement of an obstacle or its size. Accurate behavior modeling of pedestrians should include these factors. Typically, previous studies have treated pedestrian motion as a deterministic model. However, human behavior should be considered as stochastic motion even though deviations, such as walking speed, are not very large when considering local motion. When pedestrians avoid an obstacle, their motion or behavior is determined by their own individual factors, such as age, gender, etc. In this paper, we focus on the effect of obstacle speed and relative distance. To model pedestrian behavior during avoidance of a moving obstacle, walking loci were measured at first. Then, we derived and evaluated differences in avoidance behavior with respect to the obstacle speed. One result showed that the speed of a moving obstacle is one of the most important factors as the pedestrian begins avoidance, that is, the time factor that is determined by the relative speed and the relative distance affects the timing of avoidance behavior of a pedestrian.