Repair of defective 3D blade model based on deformation of adjacent non-defective cross-sectional curve

Lyu, Xuegeng

doi:10.1007/s00170-017-1393-8

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…The raw scanned point cloud data of blades are often contaminated with outliers at high curvature features due to undesirable reflections during the scanning process. To ensure an accurate assessment of the morphological parameters of the cross-sectional profile and to reduce the effect of scan data inaccuracies, Khameneifar and Feng, 28 Xiao and Huang, 29 Lu, 30 and other researchers reconstructed the blade profile with the help of B-sample generality in closed curve approximation. In order to reduce the sensitivity to outliers during curve reconstruction, some researchers tried to smooth and skeletonize the unorganized point cloud data.…”

Section: Based On Point Cloud Outlinementioning

confidence: 99%

Review: Research on product surface quality inspection technology based on 3D point cloud

Huo

Liu

Yang

et al. 2023

Advances in Mechanical Engineering

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In recent years, machine vision has played an important role in product surface quality detection. The promotion and use of this technology have largely avoided the subjectivity caused by human detection and improved detection efficiency and accuracy. Different from the image data commonly used in machine vision, point cloud can describe the spatial structure of an object, provide more information than image data, and have the ability to expand the data to build multi-dimensional data models. Due to the strong anti-interference ability of point cloud sensors and the high accuracy of three-dimensional positioning information point cloud, nondestructive testing technology based on point cloud has received more and more attention. This paper summarizes the research progress of product surface quality detection methods based on 3D point cloud in recent years. According to different data processing methods, the detection research is divided into five categories: based on point cloud contour, based on local geometric feature, based on template matching, based on multimodal point cloud, and based on deep learning. The five methods are reviewed and summarized respectively. Finally, the key problems of each detection method and the future trend of product surface quality detection technology based on 3D point cloud are discussed.

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Section: Based On Point Cloud Outlinementioning

confidence: 99%

Review: Research on product surface quality inspection technology based on 3D point cloud

Huo

Liu

Yang

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…3 Flow around leading edge for a blade with a blunt leading edge (1) and a blade with a nominal leading edge (2), based on [13] the adaptation of the defective areas to the remaining part of the blades. In most cases, this is done by interpolation or extrapolation of the non-defective areas, which ensures smooth transitions but runs the risk that the shape achieved does not meet the initial requirements [27,28]. Nearly all studies use shape recording methods, but often, these are manual.…”

Section: Introductionmentioning

confidence: 99%