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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.

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.

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).

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%.

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.

As the rapid development in electronic industries based on Printed Circuit Board (PCB) designs and high volumes manufacturing capacities and the need for high quality products with minimum defect rate comes the importance of Automated Optical Inspection (AOI) technology. The basic objectives among different AOI system manufacturers are to improve lighting, computing capability, flexibility of part staging and vision software. These improvements make AOI products more intelligent, flexible, and with far more repeatable results that are superior to human visual inspection. For finding of errors in PCB many algorithms are proposed in literature. A variety of approaches for automated visual inspection of printed circuit have been reported over the last three decades. The last reported survey in this topic introduced by Moganti96 that is why the need to introduce this survey to cover reported work after 1996. Also Moganti survey covers solely bare PCBs visual inspection. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification of these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection techniques; these also include state-of-the-art algorithms.

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.

Wireless Communications and Mobile Computing, 2022

Solder paste printing is the first part of the surface mount process flow; its postprinting defect inspection is particularly important. In this paper, we focus on studying the printing defects inspection algorithm for solder paste on PCB (Printed Circuit Board) images. e work proposes a number of methods to enhance the defects inspection performance of solder paste printing: a regional multidirectional data fusion image interpolation method, which can achieve fast and high precision image interpolation; a method for detecting solder paste areas with better accuracy, efficiency, and robustness; an improved connected domain labeling method to reduce time complexity; and defects detection and types classification method, which extracts features and centroid of every solder paste region and completes the inspection by comparing with a standard image. e experiments show that the defects inspection algorithm can detect the most common types of defects with low time consumption, high inspection accuracy, and classification accuracy.

Procedia CIRP, 2013

Testing, repair and overhaul of long-living printed circuit boards (PCBs) is a laborious task if no schematics or layout plans are available. Existing Reverse Engineering (RE) methods are time-consuming, error-prone, and destructive and require reference samples which makes them not feasible for non-OEM users of electronic devices. The Fraunhofer Institute for Production Systems and Design Technology (IPK) in Berlin and the Technical University Berlin have defined a new process for automated and non-destructive schematic and layout reconstruction based on electrical and optical measuring techniques. Current results and innovative approaches using computer vision analysis for recognition of PCB structure aiming to build error-free net lists through a net list merging algorithm are depicted in this paper.

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.

Measurement, 1993

Automated inspection of printed wiring assemblies, particularly those using surface mount technology, has become practical with the advent of high-speed vision processing for dimensional measurements on each feature coupled with artificial intelligence tools to make decisions based on the measurements. In this paper we present accuracy tests of such a system based on the measurement of known artifacts which simulate the geometry of typical solder joints.

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.

Automated Optical Inspection (AOI) Systems are commonly used on Printed Circuit Boards (PCB) manufacturing process. The use of this technology has been proved to be highly efficient on process improvements and quality achievements. The most difficult point on Surface Mounting Devices (SMD) inspection is the solder joint due to their specular reflections. Several studies have been made to improve this situation. In this paper, it is presented a solder joint classification system based on IPC-A-610D (Acceptability of Electronics Assemblies).

As the rapid development in electronic industries based on Printed Circuit Board (PCB) designs and high volumes manufacturing capacities and the need for high quality products with minimum defect rate comes the importance of Automated Optical Inspection (AOI) technology. The basic objectives among different AOI system manufacturers are to improve lighting, computing capability, flexibility of part staging and vision software. These improvements make AOI products more intelligent, flexible, and with far more repeatable results that are superior to human visual inspection. For finding of errors in PCB many algorithms are proposed in literature. A variety of approaches for automated visual inspection of printed circuit have been reported over the last three decades. The last reported survey in this topic introduced by Moganti96 that is why the need to introduce this survey to cover reported work after 1996. Also Moganti survey covers solely bare PCBs visual inspection. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification of these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection techniques; these also include state-of-the-art algorithms.

ECTI Transactions on Computer and Information Technology (ECTI-CIT), 1970

The Head Gimbal Assembly (HGA) is an essential hard disk drive (HDD) component allowing data to be read from and written to the media. Defects on the HGA may affect the data read/write process and reduce the quality of the HDD. Therefore, HGA inspection needs to be improved during HDD manufacturing. This paper describes an image processing method that automate the optical inspection of HGA solder jet ball joint defects. Vertical edge detection methods are proposed for identifying defects. The performance of the vertical edge detection method is compared to a Sobel-based method, Roberts' method and a Prewitt's method. The methods were tested with 18,123 HGA images. The experimental results show that the vertical edge detection method outperforms the other methods, which had an accuracy of 99.3%, as compared to the Sobel based method, with an accuracy of 80% and 78.2 for Roberts' method and 65.9 for Prewitt's method.

1990

Abstract Techniques used to inspect for defects on surface mount printed circuit boards (PCBs) are presented. Emphasis is placed on five different types of defects, namely missing components, misalignment, wrong orientation of IC chips, wrong parts, and poor solder joints. Five separate algorithms have been developed to detect these faults. The technique of windowing was used to reduce the amount of redundant data to be processed.

Journal of Failure Analysis and Prevention

The detection of low quality solder joint quality in hard disk drive (HDD) manufacturing is a time consuming, error-prone and costly process that is often performed manually. This paper thus proposes two automated optical solder jet ball joint defect inspection methods for head gimbal assembly (HGA) production. The first method uses a Support Vector Machine (SVM) for fault detection and the second method uses vertical edge detection to identify solder ball and pad burning defects. The methods were tested with 5,530 HGA images, and their performance was compared to a Bayesian-based method. Experimental results show that the vertical edge detection method gave the best results, with an under reject rate of 0.75% and an over reject rate of 1.88%. The accuracy of the vertical edge detection method was 98.2%, which is higher than the accuracy of 89.9% for the Bayesian-based method, and 84.6% for the SVM-based method.

Computer Modeling and Intelligent Systems

In modern instrumentation, the number of soldered joints in printed circuit boards can reach several thousand. Diagnostics of the soldered joints defects within the optical wave length range is carried out using automated diagnostic systems. A number of stages of the existing information technologies for such systems are implemented on the basis of target functions extremum search using the gradient estimation. In the large batches of products production, the use of expensive automated diagnostic systems within lighting subsystems and high cost positioning are justified. These subsystems can provide improved noise immunity. However, in conditions when small batches of products are produced, and at some stages (for example, when positioning by comparison with a standard/prototype images) in general, such objective functions can be noisy and can be multi-extremes. For such cases, information technologies based on methods of enhanced noise immunity are required. Such an increase in noise immunity can be provided by methods using wavelet transformation. For this purpose, information technologies were proposed using wavelet transformation-based procedures that improve noise immunity and reduce the error of procedures in the diagnostic systems of printed circuit boards and their soldered joints.

IEEE Transactions on Automation Science and Engineering, 2014

Automation or selective automation is adopted as a solution to most productivity problems in the hard disk drive (HDD) industry as the industry continues to grow at a 40% compounded annual growth rate. An automated production line for manufacturing the head gimbal assembly (HGA) has been developed as part of the automation solution. In the automated HGA production line, a solder jet ball (SJB) soldering station connects the suspension circuit to the slider body. We propose a Bayesian approach to automated optical inspection (AOI) of the SJB joint in the HGA process, implementing Tree Augmented Naïve Bayes Network (TAN-BN) plus check classifier in-situ using GeNIe/SMILE within the inspection software. The system is further enhanced with a result checker, achieving an overall accuracy of 91.52% with 660 production parts in a blind test. Note to Practitioners-This paper was motivated by the problem of inspecting for defective solder joints in linear, automated production line for hard disk drive parts. The size and placement of the part in the tool presented a challenge to capturing a full view of the object under inspection. Existing approaches manipulate parts of the image under different conditions. This paper suggests a method that associates the likelihood of a measured feature of the image to the quality of the solder joint produced. In this paper, we characterized the features mathematically and established a probabilistic relationship between the features and the quality of the solder joint. We then showed how the relationship can be used in real-time determination of the quality of a solder joint presented to the inspection system. We showed that the system achieved reasonable accuracy when applied to production. Index Terms-Automated optical inspection (AOI), Bayesian networks, Peter-Clark Bayesian network (PC-BN), solder-joint defect, solder-joint inspection, tree-augmented Naïve Bayesian network (TAN-BN). I. INTRODUCTION AND MOTIVATION T HE CONTINUOUS growth of digital content creation, consumption, and preservation is fueling demand for hard disk drives (HDDs).

Journal of Achievements in Materials and Manufacturing Engineering

Automatic Optical Inspection (AOI) systems that are extensively used in the industry of Electronics Manufacturing Services (EMS), performs the inspection of Surface Mount Devices (SMD). One of the main tasks of such an AOI system is to align a given PCB to the parameters of the corresponding PCB positioning system by a process called fiducial alignment. However, no detailed analysis has been carried out so far on the methodologies that can be used to have a very precise identification of PCB fiducial points. In our research, we have implemented an AOI system for the inspection of soldering defects of Through Hole Technology (THT) solder joints, which can be integrated to a desktop soldering robotic platform. Such platforms are used in environments where no specific lighting conditions can be provided within a surrounded atmosphere. Therefore, an AOI system that is capable of performing fiducial alignment of any given PCB under varying lighting condition is highly preferred. In this ...

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