Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects

The manual inspection of Printed Circuit Boards (PCB) is labor intensive and slow down the production line. During the assembly process, the defective PCBs with flux defects if not detected and remove, it can create corrosion and cause harmful effects on the board itself. As such, an automated inspection system is very much needed to overcome the aforementioned problems in PCB production line. The main objective of this work is to develop a real-time machine vision system for quality assessment of PCBs by detecting defectives PCBs. The proposed system should be able to detect flux defect on PCB board during the re-flow process and achieve good accuracy of the PCB quality checking. The proposed system is named as An Automatic Inspection System for Printed Circuit Boards (AIS-PCB), involves design and fabrication of a total automation control system involving the use of mechanical PCB loader/un-loader, robotic pneumatic system handler with vacuum cap and a vision inspection station that makes a decision either to accept or reject. The decision making part involves classifier training of PCB images. Prior to ANN training, the images need to be processed by the image processing and feature extraction. The image processing system is based on pattern matching and color image analysis techniques. The shape of the PCB pins is analyzed by using pattern matching technique to detect the PCB flux defect area. After that, the color analysis of the flux defect on a PCB boards are processed based on their red color pixel percentage in Red, Green and Blue (RGB) model. The red color filter band mean value of histogram is measured and compared to the value threshold to determine the occurrence of flux defect on the PCBs. The texture of the PCB flux defect can also be extracted based on line detection of the gradient field PCB images and feature indexing by using Radon transform-based approach. The feed-forward back-propagation (FFBP) model is used as classifier to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed for the classifier to learn and match the targets. The learned classifier, when tested on the PCBs from a factory’s production line, achieves a grading accuracy of coefficient of efficiency (COE) greater than 95%. As such, it can be concluded that the developed AIS-PCB system has shown promising results by successfully classifying flux defects in PCBs through visual information and facilitates automatic inspection, thereby aiding humans in conducting rapid inspections

The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly

Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PD

Redes neuronales artificiales para inspección óptica en control de calidad de PCB

This paper is the result of the research work on the application of an artificial neural network algorithm applied in decision making in the process of AIO (Automatic Optical Inspection) for quality control from an electronic prototyping company, generating models for the assurance of Quality in the PCB (Printed Circuit Board) product, covering the fields of decision making, quality management, production processes, neural computer systems and artificial vision among others. It is intended to develop an algorithm of artificial neural networks that provides an approach to human recognition and perception when performing a quality inspection of the final product, based on image analysis and recognition. It is presented the theoretical concepts explored and the results obtained. Initially a problem definition was made to model, then the data processing was performed, the artificial neural network model was selected to be applied, then the relevant adjustments made to the model to finally obtain a simulation and validation of the sameEste trabajo es resultado de la investigación sobre la aplicación de un algoritmo de red neuronal artificial aplicado en la toma de decisiones en el proceso de AIO (Automatic Optical Inspection) para el control de calidad de una empresa de prototipado electrónico, generando modelos para garantizar la Calidad en el Producto de PCB (Printed Circuit Board), que abarca los campos de la toma de decisiones, la gestión de calidad, los procesos de producción, los sistemas informáticos neuronales y la visión artificial, entre otros. Su objetivo es desarrollar un algoritmo de redes neuronales artificiales que proporcione un enfoque para el reconocimiento y la percepción humana al realizar una inspección de calidad del producto final, basado en el análisis y reconocimiento de imágenes. Se presentan los conceptos teóricos explorados y los resultados obtenidos. Inicialmente se hizo una definición de problema para modelar, luego se realizó el procesamiento de datos, se seleccionó el modelo de red neuronal artificial para su aplicación, luego se realizaron los ajustes pertinentes al modelo para finalmente obtener una simulación y validación de los mismos

Artificial neural networks for optical inspection in PCBS quality control

Este artículo es el resultado del trabajo de investigación acerca de la aplicación de un algoritmo de RNA (redes neuronales artificiales) para la toma de decisiones en el proceso de inspección visual automática AOI (Automatic Optical Inspeccion) para el control de calidad para empresa de prototipos electrónicos, generando modelos para el aseguramiento de la calidad en el producto PCBs (Printed Circuit Board), abarcando los campos de toma de decisiones, gestión de calidad, procesos productivos, sistemas de computación neuronal y visión artificial entre otros. Se pretende desarrollar un algoritmo de RNA que provea un acercamiento al reconocimiento y percepción humano a la hora de realizar una inspección de calidad al producto final, basado en el análisis y reconocimiento de imágenes. Se presentan los conceptos teóricos explorados y los resultados obtenidos. Inicialmente se realizó una definición del problema a modelar, a continuación, se realizó el procesamiento de los datos, se seleccionó el modelo de red neuronal artificial a aplicar, luego se realizaron los ajustes pertinentes al modelo para finalmente obtener una simulación y validación del mismo mediante el método de Histograma.This paper is the result of the research on the ANN (artificial neural network) algorithm applied in decision making in the process of AOI (Automatic Optical Inspection) for quality control for electronic prototyping company, generating models for the assurance of Quality in the PCBs (Printed Circuit Board) product, covering the fields of decision making, quality management, production processes, neural computer systems and artificial vision among others. It is intended to develop an algorithm of ANN that provides an approach to human recognition and perception when performing a quality inspection of the final product, based on image analysis and recognition. We present the theoretical concepts explored and the results obtained. Initially a problem definition was made to model, then the data processing was performed, the artificial neural network model to be applied was selected, then the relevant adjustments were made to the model to finally obtain a simulation and validation of the same by the Histogram method

AUTOMATIC OPTICAL INSPECTION-BASED PCB FAULT DETECTION USING IMAGE PROCESSING

Increased Printed Circuit Board (PCB) route complexity and density combined with the growing demand for low-scale rapid prototyping has increased the desire for Automated Optical Inspection (AOI) that reduces prototyping time and production costs by detecting defects early in the production process. Traditional defect detection method of human visual inspection is not only error prone but is also time-consuming given the growing complex and dense circuitry of modern-day electronics. Electric contact-based testing, either in the form of a bed of nails testing fixture or a flying probe system, is costly for low-rate rapid prototyping. An AOI is a non-contact test method using an image processing algorithm that quickly detects and reports failures within the PCB layer based on the captured image. A low-cost AOI system has been created using commercial off-the-shelf components specifically for low-rate production prototyping testing allowing testing of varying layers or various electronic designs without additional setup cost. Once the AOI system is physically configured, the image processing defect detection algorithm compares the test image with a defect-free reference image or by a set of pre-defined rules generated through Electronic Design and Analysis software. Detected defects are then classified into two main categories: fatal and potential. Fatal defects lead to the board\u27s rejection, while potential defects alert the operator to determine if the board should be rejected or will still satisfy pre-defined prototyping criteria. The specifications of an imaging system, camera sensor, imaging lens, and illumination set-up used in the creation of the AOI were designed considering a test PCB article already in production. The algorithm utilized is based on a non-reference defect detection method utilizing mathematical morphology-based image processing techniques to detect defects in the PCB under test

Printed Circuit Board Fault Inspection Based on Eddy Current Testing Using Planar Coil Sensor

Abstract This paper presents a printed circuit board (PCB) fault inspection method using eddy current testing generated from Helmholtz coils with a planar array-coil sensor to locate and inspect short and open faults on uniformly spaced interconnect single layer PCBs. The differences between the induced voltages from fault-free boards and faulty boards will be recorded in tables and translated into contour plots. The experimental results showed that in the presence of a short fault, the differences between the induced voltages from fault-free and faulty boards are highly negative. However, in the presence of an open fault, the differences between the induced voltages from fault free and faulty boards are highly positive. These highly positive or negative induced voltages can be translated into high density color regions on contour plots. The potential fault positions can be located by observing the color regions of the contour plots with respect to each element of the matrix sensor

FEASIBILITY INVESTIGATION OF FAULT DIAGNOSIS USING ELECTROMAGNETIC ANALYSIS OF PLANAR STRUCTURES

Nowadays, circuit design technologies have progressively advanced to cope with the high performance of the electronic components. With the circuit design advancement,the technology for IC fabrication has moved to deep submicron era. As the circuit sizes continue to scale down to nanoscale, the number of transistors and interconnects on the circuits tends to grow as well. This challengesthe circuit testing by introducing high number of possible faults on the circuit. Consequently, the product qualitycontrol has become more challenging. The product quality could drop significantly ifthe circuits are not designed to be testable

A strategy for the visual recognition of objects in an industrial environment.

This thesis is concerned with the problem of recognizing industrial objects rapidly and flexibly. The system design is based on a general strategy that consists of a generalized local feature detector, an extended learning algorithm and the use of unique structure of the objects. Thus, the system is not designed to be limited to the industrial environment. The generalized local feature detector uses the gradient image of the scene to provide a feature description that is insensitive to a range of imaging conditions such as object position, and overall light intensity. The feature detector is based on a representative point algorithm which is able to reduce the data content of the image without restricting the allowed object geometry. Thus, a major advantage of the local feature detector is its ability to describe and represent complex object structure. The reliance on local features also allows the system to recognize partially visible objects. The task of the learning algorithm is to observe the feature description generated by the feature detector in order to select features that are reliable over the range of imaging conditions of interest. Once a set of reliable features is found for each object, the system finds unique relational structure which is later used to recognize the objects. Unique structure is a set of descriptions of unique subparts of the objects of interest. The present implementation is limited to the use of unique local structure. The recognition routine uses these unique descriptions to recognize objects in new images. An important feature of this strategy is the transference of a large amount of processing required for graph matching from the recognition stage to the learning stage, which allows the recognition routine to execute rapidly. The test results show that the system is able to function with a significant level of insensitivity to operating conditions; The system shows insensitivity to its 3 main assumptions -constant scale, constant lighting, and 2D images- displaying a degree of graceful degradation when the operating conditions degrade. For example, for one set of test objects, the recognition threshold was reached when the absolute light level was reduced by 70%-80%, or the object scale was reduced by 30%-40%, or the object was tilted away from the learned 2D plane by 300-400. This demonstrates a very important feature of the learning strategy: It shows that the generalizations made by the system are not only valid within the domain of the sampled set of images, but extend outside this domain. The test results also show that the recognition routine is able to execute rapidly, requiring 10ms-500ms (on a PDP11/24 minicomputer) in the special case when ideal operating conditions are guaranteed. (Note: This does not include pre-processing time). This thesis describes the strategy, the architecture and the implementation of the vision system in detail, and gives detailed test results. A proposal for extending the system to scale independent 3D object recognition is also given

On flexibly integrating machine vision inspection systems in PCB manufacture

The objective of this research is to advance computer vision techniques and their applications in the electronics manufacturing industry. The research has been carried out with specific reference to the design of automatic optical inspection (AOI) systems and their role in the manufacture of printed circuit boards (PCBs). To achieve this objective, application areas of AOI systems in PCB manufacture have been examined. As a result, a requirement for enhanced performance characteristics has been identified and novel approaches and image processing algorithms have been evolved which can be used within next generation of AOI systems. The approaches are based on gaining an understanding of ways in which manufacturing information can be used to support AOI operations. Through providing information support, an AOI system has access to product models and associated information which can be used to enhance the execution of visual inspection tasks. Manufacturing systems integration, or more accurately controlled access to electronic information, is the key to the approaches. Also in the thesis methods are proposed to achieve the flexible integration of AOI systems (and computer vision systems in general) within their host PCB manufacturing environment. Furthermore, potential applications of information supported AOI systems at various stages of PCB manufacturing have been studied. It is envisaged that more efficient and cost-effective applications of AOI can be attained through adopting the flexible integration methods proposed, since AOI-generated information can now be accessed and utilized by other processes