An SPC artifact for automated solder joint inspection (original) (raw)
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Automatic visual solder joint inspection
IEEE Journal on Robotics and Automation, 1985
An approach is described for the automatic inspection of solder joints on printed circuit boards. Common defects are identified in solder joints and a joint is classified as being good or belonging to one of the defective classes. The motivation for this classification is not just the detection of defective joints, but the desire to automatically take corrective action on the assembly line. The features used for classification are based on characteristics of intensity surfaces. It is shown that features derived fromfacets and Gaussian curvature are effective in the classification of solder joints using a minimum-distance classification algorithm. Class separation plots are shown to be useful for quickly studying individual effectiveness of a feature or pair of features in classification. Results show the efficacy of the described approach.
Design of automatic vision-based inspection system for solder joint
Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions.
Solder joint inspection with multi-angle imaging and an artificial neural network
2008
Machine vision has been widely deployed in many industrial applications. However, for solder joint inspection, it has yet to reach the desired maturity level. This paper presents a new inspection methodology using images from both orthogonal and oblique viewing directions to the solder joint. The oblique view was made possible through a mirror pyramid. Image capturing and selection of the soldered region were done manually, but could be automated if the positional coordinates were known. Combined orthogonal and oblique gray-level images at the pixel level were directly input to an artificial neural network (ANN) for processing, eliminating the need to determine heuristic features. Learning vector quantization architecture was used as the classifier. This study was focused on geometry-related joint defects, namely, excess and insufficient. Comparisons show that the oblique view provides more useful information compared to the orthogonal view. The experimental results indicate that the proposed system has an improved recognition rate and good resilience to noise.
Towards Automatic Optical Inspection of Soldering Defects
2018 International Conference on Cyberworlds (CW), 2018
This paper proposes a method for automatic image-based classification of solder joint defects in the context of Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCBs). Machine learning-based approaches are frequently used for image-based inspection. However, a main challenge is to manually create sufficiently large labeled training databases to allow for high accuracy of defect detection. Creating such large training databases is time-consuming, expensive, and often unfeasible in industrial production settings. In order to address this problem, an active learning framework is proposed which starts with only a small labeled subset of training data. The labeled dataset is then enlarged step-by-step by combining Kmeans clustering with active user input to provide representative samples for the training of an SVM classifier. Evaluations on two databases with insufficient and shifting solder joints samples have shown that the proposed method achieved high accuracy while requiring only minimal user input. The results also demonstrated that the proposed method outperforms random and representative sampling by ~ 3.2% and ~ 2.7%, respectively, and it outperforms the uncertainty sampling method by ~ 0.5%.
A robust methodology for solder joints extraction
2013 8th International Conference on Computer Engineering & Systems (ICCES), 2013
In Electronic Manufacturing Industry, machine vision systems have been announced to outperform the electrical inspection systems effectively. It supports the Surface Mount Technology (SMT) and improves the diagnostic capabilities. The challenge there is to miniaturize components with high packing density under economic considerations. This paper presents a front-end automatic detection system tackles with the solder joint specularity, illumination variations and recognition misalignment problems. This can be achieved by enhancing the threshold-based segmentation method using Discrete Cosine (DCT).
Soldering defect detection in automatic optical inspection
Advanced Engineering Informatics, 2020
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. For the localization part, in contrast to the existing methods that are highly specified for particular PCBs, we used a generic deep learning method which can be easily ported to different configurations of PCBs and soldering technologies and also gives real-time speed and high accuracy. For the classification part, an active learning method was proposed to reduce the labeling workload when a large labeled training database is not easily available because it requires domain-specified knowledge. The experiments show that the localization method is fast and accurate. In addition, high accuracy with only minimal user input was achieved in the classification framework on two different datasets. The results also demonstrated that our method outperforms three other active learning benchmarks.
Yakoub Imad Inspection of the Integrity of Surface Mounted Integrated Circuits on a Printed Circuit Board Using Vision Master of Engineering Thesis Dublin City University, 1991
Chapter 3 deals with optics which include illumination, lenses, camera specification and inspection system interfacing. In Chapter 4 the surface mount process and inspection algorithms are highlighted, the soldering defects are mentioned, and the lighting used is illustrated. Chapter 5 is concerned with a discussion of aspects of quality control, a statement of aims, how to achieve these aims, and the inspection strategy. Chapter 6 details program structure, and organization and implementation of the inspection algorithms for soldering defects introduced in Chapter 4. The recognition algorithm uses gray-scale images to locate IC leads in the image as a first step. Having leads located, the program looks for the defects by using a fixed threshold in the first line containing leads. A full listing of the program is given in appendix A. In chapter 7 the thesis is completed with a discussion on and conclusions of the work undertaken.
3D Solder Joint Reconstruction on SMD based on 2D Images
Automated optical inspection (AOI) systems are commonly used in PCB manufacturing. The use of this technology has been proven as highly efficient for process improvements and quality achievements. The most challenging point in inspection of surface mounting devices (SMD) is the component solder joints, due to their specular reflects. Several studies have been made to improve this situation. This paper presents an algorithm for 3D solder joint reconstruction (3D-SJR). The criteria used in the classification of the solder joints was the IPC-A-610D (Acceptability of Electronics Assemblies).
Solder Paste Scooping Detection by Multi-Level Visual Inspection of Printed Circuit Boards
2011
In this paper we introduce an automated Bayesian visual inspection framework for Printed Circuit Board (PCB) assemblies, which is able to simultaneously deal with various shaped Circuit Elements (CE) on multiple scales. We propose a novel Hierarchical Multi Marked Point Process (H M MPP) model for this purpose, and demonstrate its efficiency on the task of solder paste scooping detection and scoop area estimation, which are important factors regarding the strength of the joints. A global optimization process attempts to find the optimal configuration of circuit entities, considering the observed image data, prior knowledge, and interactions between the neighboring CEs. The computational requirements are kept tractable by a data driven stochastic entity generation scheme. The proposed method is evaluated on real PCB data sets containing 125 images with more than 10.000 splice entities.