Surface reconstruction of disordered point cloud based on adaptive learning neural network

Wang, Wei; Luo, Chen; Zhou, Yijun; Zhang, Gang

doi:10.1109/iaeac54830.2022.9929956

Cited by 1 publication

(1 citation statement)

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“…In recent years, to avoid complex surface reconstruction in digital manufacturing, the black-box models of surface reconstruction based on the neural network have been proposed, such as Duan et al 16 proposed an RBF neural network mapping technology to pre-fit noise point clouds and Wang et al 17 improved the performance of fitting disordered point clouds by neural networks. These methods avoided complex algorithms and achieved fast reconstruction but introduces uncertainty and significant pre-training costs.…”

Section: Introductionmentioning

confidence: 99%

A method for fast reconstruction of closed section workpiece surface using point clouds

Li,

Song,

et al. 2024

Advances in Mechanical Engineering

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Parameterized reconstruction of workpiece surfaces is a critical step for advanced manufacturing applications, such as the solving of machining parameters, and the expression of workpiece in situ measurement. Generally, the point clouds model is easy to obtain and can be seen as the source model of the workpiece, and the B-spline surface is always taken as the ideal target parameterized model. Existing research focuses on reconstructing the single-valued surface, which can be projected onto a spatial plane without overlapping. However, the dual-valued surface, which overlaps at least twice, is also commonly encountered in industry, and has few investigations. Therefore, this paper proposes a fast reconstruction method based on reasonable segmentation and parameter domain mapping. In this approach, first, the dual-valued point clouds model is segmented into multiple single-valued point clouds models according to the sought edges in the optimal attitude. Then, the grid point coordinates of the single-valued point clouds model are calculated using the parametric-domain moving least squares method. Finally, the B-spline surface is generated by interpolating these grid points. The proposed algorithm is applied to single-bending closed-section workpieces such as the turbine blade to illustrate the effectiveness of this method. The contrast results with existing method demonstrate that the proposed approach can reconstruct the workpiece with multi-valued surfaces more quickly and more accurately, which is significant in advanced manufacturing.

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Section: Introductionmentioning

confidence: 99%