A planar-dimensions vision measurement method is proposed by developing a Neural Network to measure real-world distance between any two points on the plane. The system leveraging Neural Network ability to search in the solution space is a highly non-linear model that could map points’ location on the pixel plane of image(s) with the actual distance between them considering the non-uniform geometric distortion in captured images caused by the entocentric lens in a common camera. The method was tested with a printed calibration chessboard, placed in different locations on the plane, with measured distance between tested points. Experimental results show the proposed method’s mean absolute error is 1.24 × 10-2 mm and standard deviation is 1.63 × 10-3 mm, tested with 10-folds cross-validation method.
From 2010, the first version of Microsoft Kinect, a low-cost RGB-D camera, was released which used structured light technology to capture depth information. This device has been widely applied in many segments of the industry. In July 2014, the second version of Microsoft Kinect was launched with improved hardware. Obtaining point clouds of an observed scene with high frequency being possible leads to imaging its application to meeting the demand of 3D data acquisition. However, evaluating device capacity for mechanical part modeling has been a challenge needed to be solved. This paper intends to enhance acquired depth maps of the Microsoft Kinect v2 device for mechanical part modeling and receive an assessment about the accuracy of 3D reconstruction. Influence of materials for mechanical part modeling is also evaluated. Additionally, experimental methodology for 3D modeling of the mechanical part is finally reported to ascertain the proposed model in this paper.
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Core/shell nanowire (NW) is recognized as promising one-dimensional material for nanoelectronic and nanoelectromechanical systems. However, its mechanical properties so important for engineering applications remain largely unexplored. Based on the density functional theory (DFT), we theoretically investigate mechanical and electronic properties of the Ge-core/Si-shell NWs along the [100] direction within the cross sectional size of 1.0 nm and 1.4 nm under the axial strain. Our results show that ideal strength of Ge-core/Si-shell NWs strongly depends on wire cross sectional size compared with that of the Si and Ge NWs. Ideal strength (maximum tensile strength) of Ge-core/Si-shell NWs increases significantly when increasing thickness of the Si-shell. We found that bond lengths around interfaces between the core and the shell play a predominant role in ideal strength of Ge-core/Si-shell NWs. Additionally, band structures of NWs are modififed by applying axial strain. Band gaps of NWs decrease with increasing strain. Our results provide important insight into intrinsic mechanical behavior and electronic properties of Ge-core/Si-shell NWs, useful for the design of nanodevices with Ge-core/Si-shell NWs in future applications.
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If fatigue failure occurs during aircraft operation, it can cause catastrophic injuries. So, it is necessary to study fatigue failure at the design stage. Frequency domain fatigue analysis is used to predict fatigue failure. During frequency domain fatigue analysis, results can be calibrated by PSD analysis. In this study, fatigue failure is predicted by the Dirlik method, Lalanne method and Steinberg method. Regarding results, life determined by the Dirlik method, Lalanne method and Steinberg method were 8.737, 8.314, and 7.901 times the standard life, respectively. The Steinberg method is the most conservative but the difference with other methods was approximately 10%. In the cycle histogram, the Dirlik method had more counts than the Lalanne method in lower stress range. However, it does not affect the life of material used in this study. However, if material has a lower fatigue limit or stronger PSD data is used, life difference will occur.
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