Damage in bolts, which are used as connecting fasteners in steel structures, affects structural safety. Sophisticated machine vision methods have been formulated for the detection of loose bolts, but their accuracy remains an area for improvement. In this paper, a method based on a stacked hourglass network is proposed for automatically extracting the key points of a bolt and for obtaining the bolt loosening angle by comparing the rotations of the key points before and after the bolt is damaged. A data set containing 100 images of key bolt loosening points was collected, and rotation was performed as data augmentation to yield 1800 images. Moreover, a method was designed for automatically annotating the augmented image data set. In this study, 70%, 10%, and 20% of the annotated image data set were used for training, validation, and testing, respectively. Subsequently, a neural network model based on a stacked hourglass network was established to train the annotated image data set. The detection results were evaluated in terms of normalized errors (NEs), percentage of correct key points (PCK), detection speed, and training time. In testing, the proposed method accurately and efficiently identified the bolt loosening angle, with a PCK value as high as 99.3%. The accuracy of the proposed method was also highly robust to different shooting distances, viewing angles, and illumination levels.
Structures with multiple deformation paths provide a promising platform for robotics and reprogrammable mechanical and thermal deformation materials. Reconfigurations with a multi-path can fulfill many tasks (e.g., walking and grasping) and possess multiple properties (e.g., targeted Poisson’s ratio and thermal expansion coefficient). Here, we proposed a new ring-like kirigami structure and theoretically and experimentally found that for a basic unit, there are four discrete deformation patterns and a continuous shearing deformation pattern; thus, there are a large number of discrete deformation patterns for a multi-unit combination with geometrical compatibility coupled with a shearing deformation mode. Moreover, targeted Poisson’s ratios (either + or -) in the x- and y-directions can be realized by inversely designing the geometrical parameters for a certain deformation path. Additionally, we showed the capability of constructing 2D and 3D cellular structures in various patterns with the proposed ring-like units. The multiple deformation modes demonstrated here open up avenues to design new reprogrammable materials and robots across various scales.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.