A method for predicting the propagation life of a crack initiated on the surface of a 7075 aluminum alloy under nonproportional loading was proposed using the modified Paris law. The nonproportional loading conditions in previous studies are limited to derive an experimental formula that can estimate the effective resultant stress intensity factor range used in the modified Paris law. Therefore, crack growth tests were first conducted under new nonproportional loading conditions and the effective resultant stress intensity factor range was determined using the continuous crack opening displacement measured by digital image correlation method. Then, the contribution of the shear stress acting on the crack plane to the effective resultant stress intensity factor range was clarified by referring to well-known Findley's model, which takes into account the interlocking effect due to the shear stress acting on the crack plane. In addition, a new experimental formula that can determine the effective resultant stress range from nonproportional loading conditions without measuring the crack opening displacement was proposed. This formula enables us to predict the crack growth life until the surface crack penetrates the plate with an error of approximately ±30% based on a modified Paris-type equation through numerical integration.

Experimental investigations in a wind tunnel facility have been made over riblets and smooth surfaces at moderate Reynolds numbers under zero pressure gradient. The local wall shear stress was obtained by using the newly designed direct measurement device, and mean velocity and turbulence intensity profiles near the wall were obtained by using the aid of constant temperature anemometer and a single hot-wire probe. In the smooth surface boundary layer as canonical flow, Kármán constant κ and the coefficients of the log-law C and the velocity deficit law D were determined as κ = 0.387，C = 3.79，D = −0.515 at R_θ = 4505 and κ = 0.384，C = 3.94，D = −0.562 at R_θ = 6824. The coefficient F in the Fernholz-Finley equation is reduced due to variation in C and D at low Reynolds numbers. Trapezoidal groove riblets of 8.7 < s⁺ < 19.4 reduce the local wall shear stress by 4.4% to 7.2% compared to the smooth surface. The downward shift from the riblet top to the virtual origin of the wall shear stress h_pτw to the surface for uniform shear stress coincides with the downward shift from the riblet top to the virtual origin of the longitudinal mean velocity h_pl for similarity requirement. The estimated distance from riblet top to the virtual origin of cross flow h_pc is slightly smaller than h_pl. The maximum value of the turbulence intensity nondimensionalized by the friction velocity of the smooth surface is certainly decreased over riblets, but the maximum value nondimensionalized by each friction velocity is almost same over smooth and riblets surfaces.

There is a growing demand for robots that can assist the elderly to stand up independently. To this end, it is important for the robots to estimate the user's condition and provide appropriate assistance. In particular, the robots are required to be able to detect abnormal conditions and prevent accidents. However, accurate measurement of human posture requires a large number of sensors, making the system complex and difficult to handle. To estimate the state of a care robot user using a small number of sensors, we have proposed a method to calculate the position of the center of gravity as candidates. However, because the estimation was performed simply by using machine learning, it did not sufficiently consider how the center of gravity candidates changes depending on the state of the robot user. Therefore, it was necessary to measure not only data of normal operation but also data of abnormal states for binary classification. In this study, we propose and validate a method to detect abnormalities in standing without anomaly state data by analyzing how the center of gravity candidates tends to change over time in normal and abnormal standing. The analysis shows that the maximum value of the candidate center of gravity in the forward direction changes sharply when abnormal standing occurs. Therefore, anomaly detection was performed using the value of the second-order derivative of the maximum value of y of the candidate center of gravity, and abnormal standing was correctly detected.

In this study, we examined the effects of introducing a First-Order Hold (FOH) on the “unobservable residual vibrations caused by mechanical resonances near the sampling frequency” in sampled-data control systems. We compared three types of holds used in sampled-data control systems: Zero-Order Hold (ZOH), Causal First-Order Hold (CFOH), and Triangle-method based First-Order Hold (TFOH). First, we conducted an analysis of the Shock Response Spectrum (SRS) using a half-sine wave and demonstrated that changing from ZOH to FOH can suppress the occurrence of residual vibrations near the sampling frequency. Next, we performed SRS analysis using an acceleration polynomial that considers the characteristics of the hold, and obtained results similar to those from the half-sine wave analysis. Finally, we conducted simulation verification of feedforward control using a model that mimics a precision positioning device, demonstrating the validity of the SRS analysis results. From these results, it was clarified that the “unobservable residual vibrations caused by mechanical resonances near the sampling frequency” in sampled-data control systems can be reduced by employing FOH instead of ZOH. Furthermore, it was shown that the reduction effect is greater when TFOH is employed compared to CFOH.

As machines become more sophisticated and manufacturing processes more complex in the manufacturing industry, there is a need to build simple systems that enable condition monitoring without specialized knowledge. Therefore, AI-based condition monitoring is attracting attention. To improve the accuracy and interpretation of sliding surface condition monitoring, this study aims to elucidate the relationship between wear mechanisms and data obtained from each sensor such as friction coefficient, acoustic emission, vibration acceleration, and contact electrical resistance. In this paper, we investigated a method to reduce the opacity of anomaly detection using machine learning and explored sensor data useful for condition monitoring of the sliding surface. The usefulness of SHAP (SHapley Additive exPlanations), one of the explanatory methods of machine learning, was verified by using it on the analysis results of anomaly detection. As a result, it was found that SHAP is useful as a method to give interpretation to the analysis results of machine learning for monitoring the condition of sliding surfaces, because the condition of sliding surfaces over time, which cannot be understood by unsupervised learning, can be understood in more detail than before based on the contribution of each sensor data. In addition, a comparison of the contribution of each sensor data to the wear behavior for each sliding distance and the predictions by SHAP revealed the possibility that the data from the vibration acceleration sensor mounted perpendicularly to the sliding direction might effectively detect signs of seizure.

In the present report, we focused on high reduction ratio hypoid gear with a three-tooth pinion and its meshing performance. First, we monitored the tooth surface temperature in meshing teeth, based on combined image from temperature images with a high-speed-camera thermography, and compared it with tooth contact image with a red paint. Moreover, we proposed a predicting method of tooth contact area from a combined temperature image on the tooth surface. Second, we estimated its real contact ratio based on measuring the tooth root stresses at the toe, middle and heel positions. Third, we measured the meshing transmission error (MTE) between the gear and the pinion, and the rotational acceleration at the gear outer in meshing teeth. As a result, it could be seen that the real contact ratio affected tooth surface temperature and MTE. Regarding a high reduction ratio hypoid gear, the deformation at the gear rim also affected the dynamic vibration in meshing gear and pinion teeth.

In gymnastics, there is a problem in which Japanese athletes who perform well in Japan are unable to perform as well as they would like in practice or competitions overseas. The manufacturer of an apparatus used varies depending on competitions, and the feel of an apparatus may differ from the apparatus normally used in practice, it is difficult to perform as usual. It is necessary for athletes to improve their own technique to perform well, and how to use an apparatus is also an important factor, especially in horizontal bar competitions. Because of different types of an apparatus, athletes who perform well in domestic competitions tend to be unable to perform as well as they would like, and their scores do not improve. As can be seen from the differences in the feeling of use that athletes feel during competition, structural characteristics of each manufacturer's apparatus can’t be accurately evaluated. FIG Apparatus Norms is difficult to measure, so if it is possible to accurately evaluate the feeling felt by athletes, it may be possible to further minimize the differences between apparatus manufacturers, leading to improved skills and higher scores in competitions. In this paper, we conduct a quasi-static load test with the bar displacement every 15[deg] from the gravity direction to the horizontal direction on a gymnastics horizontal bar apparatus. Based on the results, we will investigate quantitative evaluation methods for the structural characteristics of a gymnastics horizontal bar apparatus. We will also examine the effects of cables tension on characteristics of a gymnastics horizontal bar apparatus using evaluation methods.

Recently, machine learning has been applied as a powerful tool for determining cell functions and states from cell shape features in microscopic images. However, it has not been fully investigated how changes in the microscopic observation environments and differences in cell types affect the accuracy of cell detection by machine learning. In addition to cell shapes, changes in intracellular structure are also considered important information in determining the cell functions. In particular, the shape of the cell nucleus is expected to be an indicator for disease diagnosis. In this study, we established a method for detecting the cell nuclear regions in phase contrast (PC) microscope images of unstained cells using Usiigaci (Tsai et al., 2019), an image machine learning platform consisting of Mask R-CNN. From a practical point of view, we investigated how the effects of out-of-focus state of the microscope and the cell type differences on the accuracy of nuclear region detection in PC microscope images. It was found that the accuracy of the nucleus segmentation was significantly influenced by the contrast (brightness gradient) of the captured images, and that an image taken at least within the depth of focus of the observation system is necessary for the accurate nucleus segmentation. Furthermore, the nucleus segmentation is easily affected by differences in cell morphology and intracellular structures such as the cytoskeletons, and the accuracy of the nucleus segmentation is less than half for vascular smooth muscle cells compared to NIH3T3 fibroblast cell lines. Therefore, compared to conventional segmentation for cell outlines, the nucleus segmentation using label-free PC microscope images is extremely difficult, and these are important issues that cannot be ignored in the practical application of machine learning of cell microscopic images.

The purpose of this paper is to present a new open innovation type public-private partnership scheme in the space sector, which is suitable for the space industrial complex in Japan, to promote R&D and to realize national projects by public R&D agencies, and to create commercial business by private companies simultaneously. Compared to other sectors, system development and business creation in the space sector remains difficult. Then typical public-private partnership methods from other industries cannot be applied to the space sector, although the U.S. and other countries are promoting public-private partnerships in the space sector. And the more private sector support is provided, the more it will affect the R&D capabilities of public R&D space agencies. Furthermore, Japan has a large gap with the U.S. in terms of technological infrastructure and budget, making it difficult to apply the space public-private partnership methods of the U.S. The scheme proposed in this paper is based on a technology-circulating collaboration model applicable to the Japanese machinery manufacturing industry in general, with modifications to adapt it to the space industrial complex in Japan and to help solve the above issues. Then, a case study analysis is presented to illustrate the suitability of this scheme.