Browsing by Author "Wang, Yizhong"
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Item Open Access A fiber-guided motorized rotation laser scanning thermography technique for impact damage crack inspection in composites(IEEE, 2023-04-11) Liu, Haochen; Tinsley, Lawrence; Deng, Kailun; Wang, Yizhong; Starr, Andrew; Chen, Zhenmao; Zhao, YifanLaser Thermography manifests superior sensitivity and compatibility to detect cracks and small subsurface defects. However, the existing related systems have limitations on either inspection efficiency or unknown directional cracks due to the utilization of stationary heat sources. This article reports a Fiber-guided Motorised Rotation Laser-line Scanning Thermography (FMRLST) system aiming to rapidly inspect cracks of impact damage with unknown direction in composite laminates. An optical head with fibre delivery integrated with a rotation motor is designed and developed to generate novel scanning heating in a circumferential rotation manner. A FEM model is first proposed to simulate the principle of FMRLST testing and produce thermograms for the development of post-processing methods. A damage enhancement method based on Curvelet Transform is developed to enhance the visualization of thermal features of cracks, and purify the resulting image by suppressing the laser-line heating pattern and cancelling noise. The validation on three composite specimens with different levels of impact damage suggests the developed FMRLST system can extract unknown impact surface cracks efficiently. The remarkable sensitivity and flexibility of FMRLST to arbitrary cracks, along with the miniaturized probe-like inspection unit, present its potential in on-site thermographic inspection, and its design is promising to push the LST towards.Item Open Access A full 3D reconstruction of rail tracks using a camera array(Elsevier, 2023-12-14) Wang, Yizhong; Liu, Haochen; Yang, Lichao; Durazo-Cardenas, Isidro; Namoano, Bernadin; Zhong, Cheng; Zhao, YifanThis research addresses limitations found in existing 3D track reconstruction studies, which often focus solely on specific rail sections or encounter deployment challenges with rolling stock. To address this challenge, we propose an innovative solution: a rolling-stock embedded arch camera array scanning system. The system includes a semi-circumferential focusing vision array, an arch camera holder, and a Computer Numerical Control machine to simulate track traverse. We propose an optimal configuration that balances accuracy, full rail coverage, and modelling efficiency. Sensitivity analysis demonstrates a reconstruction accuracy within 0.4 mm when compared to Lidar-generated ground truth models. Two real-world experiments validate the system's effectiveness following essential data preprocessing. This integrated technique, when combined with rail rolling stocks and robotic maintenance platforms, facilitates swift, unmanned, and highly accurate track reconstruction and surveying.