Abstract
The 3D printing process lacks real-time inspection, which is still an open-loop manufacturing process, and the molding accuracy is low. Based on the 3D reconstruction theory of machine vision, in order to meet the applicability requirements of 3D printing process detection, a matching fusion method is proposed. The fast nearest neighbor (FNN) method is used to search matching point pairs. The matching point information of FFT-SIFT algorithm based on fast Fourier transform is superimposed with the matching point information of AKAZE algorithm, and then fused to obtain more dense feature point matching information and rich edge feature information. Combining incremental SFM algorithm with global SFM algorithm, an integrated SFM sparse point cloud reconstruction method is developed. The dense point cloud is reconstructed by PMVs algorithm, the point cloud model is meshed by Delaunay triangulation, and then the accurate 3D reconstruction model is obtained by texture mapping. The experimental results show that compared with the classical SIFT algorithm, the speed of feature extraction is increased by 25.0%, the number of feature matching is increased by 72%, and the relative error of 3D reconstruction results is about 0.014%, which is close to the theoretical error.
Highlights
The emerging additive manufacturing methods represented by 3D printing have changed the traditional manufacturing mode [1,2,3]. 3D printing has the advantages of rapid prototyping, simple use, low cost, and high material utilization [4,5,6]
The following conclusions can be drawn from the comparative analysis of the test results: Compared with FFT-SIFT algorithm alone, the fusion algorithm of FFT-SIFTAKAZE increases the number of feature matches by 72.0%, which is far higher than the number of feature points extracted by the SIFT and accelerated KAZE algorithm (AKAZE) algorithms
Based on the vision 3D measurement theory, this paper proposes a high-precision and rapid 3D reconstruction method of 3D printing process based on vision, and designs the corresponding detection structure
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Existing detection methods for 3D printing process parts mainly use indirect detection. Compared with the existing panoramic 3D reconstruction methods, the monocular vision 3D reconstruction method based on structure motion recovery (SFM) is suitable for 3D detection in the 3D printing process because of its low cost, simple structure and high reconstruction accuracy [15,16].The process of 3D reconstruction based on SFM generally includes image acquisition preprocessing, feature point extraction and matching, 3D sparse point cloud densification and surface texture reconstruction. Most of the 3D printed parts are of single color, and require high robustness and accuracy in the printing process detection, so the existing feature extraction algorithms are not fully applicable. The research laid a foundation for online quality monitoring and control of 3D printing process
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