Prof. Lianpo Wang Profile

Prof. Lianpo Wang

at Northwestern Polytechnical Univ

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Proceedings Article | 27 January 2023 Paper

A torsional axis measurement method based on the adaptive window shape DIC

Lianpo Wang, Feng Yahong

Proceedings Volume 12550, 1255013 (2023) https://doi.org/10.1117/12.2666580

KEYWORDS: Deformation, Digital image correlation, Windows, Cameras, Imaging systems, Image processing, Sensors, Charge-coupled devices, Technology, Digital imaging, Angle measurement

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Torsion test is one of the most basic tests in material mechanics, which can reflect the static and dynamic properties of materials. Torsional deformation measurement, as a key technology in torsion test, has received more and more attention in the field of mechanical engineering. Although there have been many measurement methods for measuring torsional deformation, these methods suffer from various disadvantages. To realize the torsion measurement of large torsional deformation, this paper proposes a torsion measurement method based on the Digital Image Correlation (DIC) method, which has the advantages of full-field, large-scale, and non-contact measurement. First, to solve the de-correlation problem caused by large torsional deformation, this paper proposed an adaptive window shape based DIC method, which adaptively changes the window shape with the torsion of the specimen to ensure the correlation between the reference and deformed sub-region at a high level. Second, the monocular camera was used to photograph the specimen from six directions to obtain six reference images, and the reverse matching method was used to overcome the problem of tracking points moving out of the camera's field of view. Experiment shows that, the adaptive window shape based monocular DIC method can effectively measure the large torsional deformation.

Proceedings Article | 19 December 2022 Presentation + Paper

High-accuracy digital volume correlation-based point cloud registration for 3D reconstruction

Wei Shi, Lianpo Wang

Proceedings Volume 12319, 1231907 (2022) https://doi.org/10.1117/12.2642068

KEYWORDS: Point clouds

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Three-dimensional (3D) reconstruction plays an important role in intelligent manufacturing, industrial inspection and reverse modeling. The model accuracy of 3D reconstruction has important influence on the final product quality and reliability, and point cloud registration is the key to 3D reconstruction, whose registration accuracy directly affects the final 3D reconstruction accuracy. There are many researches on point cloud registration algorithms, but the existing point cloud methods often have the disadvantages of low accuracy and slow speed when registering large point clouds. To meet this challenge, it is proposed that a high-accuracy point cloud registration method by digital volume correlation (DVC). Firstly, source point cloud and target point cloud are down sampled by voxel grid filter. Subsequently, the intrinsic shape signatures (ISS) feature is used to extract the feature points and random sample consensus (RANSAC) algorithm is used for coarse registration with ISS feature points. Finally, the point clouds are converted into voxels with gray-value information, which will be used for DVC calculation to obtain higher accuracy point cloud registration results. Experimental results show that our method can achieve high precision registration of large point clouds and ensure sufficient registration speed.

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