A Novel Scratch Detection and Measurement Method for Automotive Stamping Parts

Liu, Changying; An, Bo-Wen; Hou, Yuguang; Wang, Hao; Liu, Yang

doi:10.1109/tim.2022.3193970

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…In order to verify the effectiveness of our method, this paper also uses a variety of commonly used segmentation algorithms for experimental comparison of point cloud segmentation. In this paper, we used the model-based segmentation method: RANSAC [31], region-based segmentation method: Region Growing [32], clustering-based segmentation method: DBSCAN [33], Euclidean clustering [35] and deep learning-based method: PointNet++ [36]. Where the model parameters of PointNet++ were pre-trained in shape-net40.…”

Section: Point Cloud Segmentation Results Of Bonding Wires and Compar...mentioning

confidence: 99%

“…In the original point cloud, noisy points and bonding wire point clouds are clustered together, which is challenging for point cloud segmentation. Traditional point cloud segmentation methods such as RANSAC [31], region growing [32], and DBSCAN [33] accomplish the segmentation of point cloud regions directly based on the difference of point cloud features. The above methods do not try to reduce the density of noise points to improve the segmentation accuracy.…”

Section: Methodsmentioning

confidence: 99%

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3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

Qiao

et al. 2023

CAAI Trans on Intel Tech

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Non‐destructive detection of wire bonding defects in integrated circuits (IC) is critical for ensuring product quality after packaging. Image‐processing‐based methods do not provide a detailed evaluation of the three‐dimensional defects of the bonding wire. Therefore, a method of 3D reconstruction and pattern recognition of wire defects based on stereo vision, which can achieve non‐destructive detection of bonding wire defects is proposed. The contour features of bonding wires and other electronic components in the depth image is analysed to complete the 3D reconstruction of the bonding wires. Especially to filter the noisy point cloud and obtain an accurate point cloud of the bonding wire surface, a point cloud segmentation method based on spatial surface feature detection (SFD) was proposed. SFD can extract more distinct features from the bonding wire surface during the point cloud segmentation process. Furthermore, in the defect detection process, a directional discretisation descriptor with multiple local normal vectors is designed for defect pattern recognition of bonding wires. The descriptor combines local and global features of wire and can describe the spatial variation trends and structural features of wires. The experimental results show that the method can complete the 3D reconstruction and defect pattern recognition of bonding wires, and the average accuracy of defect recognition is 96.47%, which meets the production requirements of bonding wire defect detection.

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Section: Point Cloud Segmentation Results Of Bonding Wires and Compar...mentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

Qiao

et al. 2023

CAAI Trans on Intel Tech

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“…Liu et al [24] proposed a novel method for detecting and measuring scratches on automotive stamping parts utilizing the geometric features of point clouds. Experimental research was conducted on two stamped parts with scratches, demonstrating the effectiveness of the proposed method.…”

Section: Methods Based On Point Cloud Geometry Featurementioning

confidence: 99%

A novel method for necking detection and measurement in automotive sheet metal components

Li,

Wang,

Zhang

et al. 2024

Meas. Sci. Technol.

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In this article, a novel necking detection and measurement method for automotive sheet metal components is proposed to detect and measure necking. The proposed method utilizes a point cloud registration-based approach to achieve better defect detection performance in the region of interest. In this method, an efficient registration algorithm called GF-ICP is introduced, which exhibits excellent performance for complex surfaces, such as sheet metal parts. Subsequently, an algorithm called NVP is proposed for defect point cloud detection and extraction, enabling the acquisition of comprehensive necking information. Finally, a necking dimension measurement method, referred to as TM-RDE, is introduced to measure the surface area and depth of necking. Experimental investigations were conducted on two sheet metal components with necking, and comparisons were made with other methods. The results demonstrate the practicality and effectiveness of this proposed method.

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A Novel Scratch Detection and Measurement Method for Automotive Stamping Parts

Cited by 2 publications

References 33 publications

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

3D reconstruction and defect pattern recognition of bonding wire based on stereo vision

A novel method for necking detection and measurement in automotive sheet metal components

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