Digital Inspection Technology for Sheet Metal Parts Using 3D Point Clouds
To solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4827 |
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| author | Jian Guo Dingzhong Tan Shizhe Guo Zheng Chen Rang Liu |
| author_facet | Jian Guo Dingzhong Tan Shizhe Guo Zheng Chen Rang Liu |
| author_sort | Jian Guo |
| collection | DOAJ |
| description | To solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by using a K-dimensional tree (KD tree). The pass-through filtering method is adopted to denoise the point cloud data. To preserve the fine features of the parts, an improved voxel grid method is proposed for the downsampling of the point cloud data. Feature points are extracted via the intrinsic shape signatures (ISS) algorithm and described using the fast point feature histograms (FPFH) algorithm. After rough registration with the sample consensus initial alignment (SAC-IA) algorithm, an initial position is provided for fine registration. The improved iterative closest point (ICP) algorithm, used for fine registration, can enhance the registration accuracy and efficiency. The greedy projection triangulation algorithm optimized by moving least squares (MLS) smoothing ensures surface smoothness and geometric accuracy. The reconstructed 3D model is projected onto a 2D plane, and the actual dimensions of the parts are calculated based on the pixel values of the sheet metal parts and the conversion scale. Experimental results show that the measurement error of this inspection system for three sheet metal workpieces ranges from 0.1416 mm to 0.2684 mm, meeting the accuracy requirement of ±0.3 mm. This method provides a reliable digital inspection solution for sheet metal parts. |
| format | Article |
| id | doaj-art-dbfe2545e4dd43f18d540b228f5745e4 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-dbfe2545e4dd43f18d540b228f5745e42025-08-20T03:36:30ZengMDPI AGSensors1424-82202025-08-012515482710.3390/s25154827Digital Inspection Technology for Sheet Metal Parts Using 3D Point CloudsJian Guo0Dingzhong Tan1Shizhe Guo2Zheng Chen3Rang Liu4College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaTo solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by using a K-dimensional tree (KD tree). The pass-through filtering method is adopted to denoise the point cloud data. To preserve the fine features of the parts, an improved voxel grid method is proposed for the downsampling of the point cloud data. Feature points are extracted via the intrinsic shape signatures (ISS) algorithm and described using the fast point feature histograms (FPFH) algorithm. After rough registration with the sample consensus initial alignment (SAC-IA) algorithm, an initial position is provided for fine registration. The improved iterative closest point (ICP) algorithm, used for fine registration, can enhance the registration accuracy and efficiency. The greedy projection triangulation algorithm optimized by moving least squares (MLS) smoothing ensures surface smoothness and geometric accuracy. The reconstructed 3D model is projected onto a 2D plane, and the actual dimensions of the parts are calculated based on the pixel values of the sheet metal parts and the conversion scale. Experimental results show that the measurement error of this inspection system for three sheet metal workpieces ranges from 0.1416 mm to 0.2684 mm, meeting the accuracy requirement of ±0.3 mm. This method provides a reliable digital inspection solution for sheet metal parts.https://www.mdpi.com/1424-8220/25/15/4827digital measurementpoint cloud datapoint cloud registrationthree-dimensional reconstruction |
| spellingShingle | Jian Guo Dingzhong Tan Shizhe Guo Zheng Chen Rang Liu Digital Inspection Technology for Sheet Metal Parts Using 3D Point Clouds Sensors digital measurement point cloud data point cloud registration three-dimensional reconstruction |
| title | Digital Inspection Technology for Sheet Metal Parts Using 3D Point Clouds |
| title_full | Digital Inspection Technology for Sheet Metal Parts Using 3D Point Clouds |
| title_fullStr | Digital Inspection Technology for Sheet Metal Parts Using 3D Point Clouds |
| title_full_unstemmed | Digital Inspection Technology for Sheet Metal Parts Using 3D Point Clouds |
| title_short | Digital Inspection Technology for Sheet Metal Parts Using 3D Point Clouds |
| title_sort | digital inspection technology for sheet metal parts using 3d point clouds |
| topic | digital measurement point cloud data point cloud registration three-dimensional reconstruction |
| url | https://www.mdpi.com/1424-8220/25/15/4827 |
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