Existing PCB solder joint defect detection algorithms struggle to satisfy the concurrent demands of high accuracy, low false alarm rate, and high speed. To address this challenge, this paper ...
The cross section is marked with a red curve in Fig. 9. The shape defects detection of solder joints is the original intention of this work, while connected welding solder joints are usually detected by electrical inspection.
Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized. What is more, the maximal class variance method (OTSU) is used to distinguish solder joints from background, and the Morphology was used to extract the suspected solder joints.
The interface receives the scores of solder joints from the defect detection module. The interface also queries the SQL database to obtain the location of solder joints and the point clouds of PCBs. The PCBs are displayed in a 3D interactive module and the scores are shown in a list control. We annotate the solder joints with different 3D boxes.
Besides, a unique solder joint dataset based on point clouds is created from real industry to evaluate the performance of the proposed model. The validity of the attended region is demonstrated by the manual feature FPFH. In the future, we plan to detect the position of solder joints on PCBs by deep neural networks.
Electronic chips are usually soldered on a printed circuit board (PCB) with solder joints in the semiconductor industry. The process of soldering may occur various defects, such as multi-tin, less-tin, connected welding, and tin up. Defective solder joints are costly defects for both electronic equipment suppliers and customers.
Existing PCB solder joint defect detection algorithms struggle to satisfy the concurrent demands of high accuracy, low false alarm rate, and high speed.
Existing PCB solder joint defect detection algorithms struggle to satisfy the concurrent demands of high accuracy, low false alarm rate, and high speed. To address this challenge, this paper ...
We proposed to use the deep learning method YOLO to locate hundreds of small and closely spaced solder joints in PCB images automatically. Our experiment shows that it can be easily ported to different PCB layouts and also achieved accurate detection results with a very high speed of less than 0.34 s per PCB image (110 solder joints).
Aiming at the problem that the existing PCB plug-in solder defect detection algorithms cannot meet the requirements of high precision, low false alarm rate, and high speed at the same time,...
We proposed to use the deep learning method YOLO to locate hundreds of small and closely spaced solder joints in PCB images automatically. Our experiment shows that it …
In this paper, we propose a novel neural network named double-flow region attention network (DoubRAN) to detect defects of solder joints with 3D point clouds. On the one hand, a binocular lidar system is designed to efficiently capture 3D point clouds of solder joints.
To improve both inspection efficiency and accuracy, in this work, we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments.
We can achieve the table of solder joints quality characteristic parameters and training samples of solder joints intelligent identification by choosing the four key factors of...
limitations, operator handling or placement, solder joint formation, effectiveness of inspection methods like x-ray or in-circuit-test (ICT) as well as reliability concerns such as product life, circuit board retention and harsh environment resistance. Solder Charge SMT technology is a new PCB solder attach method introduced for the high density interconnect market to improve on …
Printed circuit boards (PCBs) are an essential component of electronic products, and detecting solder joint defects is critical in the PCB production process. Machine vision technology allows detection with high efficiency and cost-effectiveness. Therefore, this paper summarizes the basic principles of image processing-based and machine ...
In this paper, we propose a novel neural network named double-flow region attention network (DoubRAN) to detect defects of solder joints with 3D point clouds. On the …
The components are then subjected to thermal stress and humidity to determine the reliability of the solder joints. This test can detect issues such as component float, insufficient solder coverage, or solder voids, which can affect the performance and lifespan of the PCB. Wetting Balance Analysis. Wetting balance analysis is another commonly used method for evaluating the …
To improve both inspection efficiency and accuracy, in this work, we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in …
The combination of strain and temperature during thermomechanical fatigue has a large effect on the microstructure and microstructural evolution of solder joints [5], [6].Strain concentration enhances diffusion, leading to microstructural coarsening at elevated temperatures [7] has been observed that typically only a fraction of the solder joint cross-section actually …
Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized. What is more, the maximal class variance method (OTSU) is used to distinguish solder joints from background, and the Morphology was used to extract the suspected solder joints ...
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As we can find mostly solder bridge in these defects, we pick up to detect solder bridge in a production line. The problems of image analysis for the detection of defects at BGA solder joints are the detection accuracy and image processing time according to a production line speed. To get design data for the development of the inspection system ...
Two artificial intelligence (AI) based models are proposed and compared for joint defect detection. The noised ROI problem and the varying sizes of imaging dimension problem are addressed. …
Two artificial intelligence (AI) based models are proposed and compared for joint defect detection. The noised ROI problem and the varying sizes of imaging dimension problem are addressed. The efficacy of the proposed methods are verified through experimenting on a real-world 3D X-ray dataset. By incorporating the proposed methods, specialist ...
This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both …
Abstract—This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both …
: failure criterion, solder joint, interconnection, reliability, control chart . 1. Introduction. One of the challenges in an experimental study of solder joint reliability is to determine when cracks occur in a solder joint. The most common way is through measurement of resistance solder joint or a daisy a chain. This method is based on the
Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized. What is more, the maximal class variance method (OTSU) is used to distinguish solder joints from background, and the Morphology was used to extract the suspected solder joints. The positive and negative …
Abstract—This paper proposes an integrated detection framework of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Both localization and classifications tasks were considered.
Printed circuit boards (PCBs) are an essential component of electronic products, and detecting solder joint defects is critical in the PCB production process. Machine vision technology allows …
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