A two-stage knowledge-based deep learning algorithm is presented for enabling automated damage detection in real-time using the augmented reality smart glasses. The first stage of the algorithm entails the identification of damage prone zones within the region of interest. This requires domain knowledge about the damage as well as the structure being inspected. In the second stage, automated damage detection is performed independently within each of the identified zones starting with the one that is the most damage prone. For real-time visual inspection enhancement using the augmented reality smart glasses, this two-stage approach not only ensures computational feasibility and efficiency but also significantly improves the probability of detection when dealing with structures with complex geometric features. A pilot study is conducted using hands-free Epson BT-300 smart glasses during which two distinct tasks are performed: First, using a single deep learning model deployed on the augmented reality smart glasses, automatic detection and classification of corrosion/fatigue, which is the most common cause of failure in high-strength materials, is performed. Then, in order to highlight the efficacy of the proposed two-stage approach, the more challenging task of defect detection in a multi-joint bolted region is addressed. The pilot study is conducted without any artificial control of external conditions like acquisition angles, lighting, and so on. While automating the visual inspection process is not a new concept for large-scale structures, in most cases, assessment of the collected data is performed offline. The algorithms/techniques used therein cannot be implemented directly on computationally limited devices such as the hands-free augmented reality glasses which could then be used by inspectors in the field for real-time assistance. The proposed approach serves to overcome this bottleneck.
The powder process is dynamic at the microscale level. Due to the capillary effect, the viscous attraction of the liquid phase between particles alters the strength and stability of wet granular materials. In this paper, experiments on the rupture behavior of the funicular liquid bridge between three rigid spheres have been presented. The results show that the peak force and rupture distance of the funicular liquid bridges are affected by the size and relative position of the spheres, volume and viscosity of the liquid, and separation rate; the coalescence of the liquid bridges causes a decrease in peak force and an increase in rupture distance, and the rupture distance shows a nonmonotonic functional correlation with the separation rate. Finally, an empirical equation of the correlation between the rupture distance and liquid volume is proposed, whose rationality is verified by comparing it with the results of the existing models.
The
mild and efficient hydroxytrifluoromethylation of alkenes with
bromotrifluoromethane (CF3Br) and atmospheric oxygen mediated
by cobalt-tertiary amine is described. This reaction proceeds with
broad substrate scope and good functional group compatibility. Mechanistic
studies indicate that the reaction proceeds through a radical pathway,
which is enabled by combination of the previously unexplored highly
efficient N-isopropyl-N,2-dimethylpropan-2-amine
with Co(II) for the single electron reduction of CF3Br
to CF3 radical.
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