[en] The cable which transfers the signal and power in an industrial robot has a problem of fatigue fracture like steel components. Since the cable is very flexible compared to other components of the system, it is difficult to estimate its motion numerically. Some studies have been done on a large deformation problem, especially in a cable, and a few attempts have been made to apply the absolute nodal coordinate formulation (ANCF), which can simulate a large deformation. Only researches about the fatigue life of structural cables or comparative studies of FEM and ANCF simulations can be found. This paper presents a method to simulate the behavior of the cable harness using the ANCF and to predict the fatigue life while computing the strain time history of the point of interest. Rigid body dynamics is applied for the robot system, while ANCF is used for the cable harness. The simulation is performed by using the dynamic analysis process. The material property of the cable is obtained by a test. A simplified model is prepared. With these data, the behavior of the cable is simulated and the fatigue life is predicted

[en] Recently, engineering applications have started to adopt solutions inspired by nature. The peculiar adhesive properties of gecko skin are an example, as they allow the animal to move freely on vertical walls and even on ceilings. The high adhesive forces between gecko feet and walls are due to the hierarchical microscopical structure of the skin. In this study, the effect of metal coatings on the adhesive strength of synthetic, hierarchically structured, dry adhesives was investigated. Synthetic dry adhesives were fabricated using PDMS micro-molds prepared by photolithography. Metal coatings on synthetic dry adhesives were formed by plasma sputtering. Adhesive strength was measured by pure shear tests. The highest adhesion strengths were found with coatings composed of 4 nm thick layers of Indium, 8 nm thick layers of Zinc and 6 nm thick layers of Gold, respectively

[en] Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray features extracted by deep learning to alert high-risk osteoporosis populations. We retrospectively used data obtained from health check-ups including spine X-ray and dual-energy X-ray absorptiometry (DXA). Consecutively, we selected people with normal and abnormal bone mineral density. From the regions of interest of X-ray images, deep convolutional networks were used to generate image features. We designed prediction models for abnormal BMD using the image features trained by machine learning classification algorithms. The performances of each model were evaluated. From 334 participants, 170 images of abnormal (T scores < − 1.0 standard deviations (SD)) and 164 of normal BMD (T scores > = − 1.0 SD) were used for analysis. We found that a combination of feature extraction by VGGnet and classification by random forest based on the maximum balanced classification rate (BCR) yielded the best performance in terms of the area under the curve (AUC) (0.74), accuracy (0.71), sensitivity (0.81), specificity (0.60), BCR (0.70), and F1-score (0.73). In this study, we explored various machine learning algorithms for the prediction of BMD using simple spine X-ray image features extracted by three deep learning algorithms. We identified the combination for the best performance in predicting high-risk populations with abnormal BMD.