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

On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs

Pre-print of an original work presented at KES-IDT 2021 held virtually.We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. We then present the design and results of experimentation on combinations of rotations and perspectives used during training and the resulting impact on test accuracy. We then show when and how well data augmentation techniques are capable of simulating rotations vs. perspectives not present in the training data. We perform all experiments using CNNs with and without homogeneous vector capsules (HVCs) and investigate and show the capsules' ability to better encode the equivariance of the sub-components of the micro-PCBs. The results of our experiments lead us to conclude that training a neural network equipped with HVCs, capable of modeling equivariance among sub-components, coupled with training on a diversity of perspectives, achieves the greatest classification accuracy on micro-PCB data

Sistema de inspeção visual de placas de circuito impresso

A inspeção de qualidade de produtos na Indústria Brasileira ainda é manual ou pouco automatizada. Através da automatização dessa tarefa, é possível aumentar a produtividade e a qualidade do produto final. Com esse intuito, este trabalho visa desenvolver uma solução de inspeção automática visual de placas de circuito impresso. Através de imagens capturadas de placas de circuito impresso e de técnicas de processamento digital de imagens, é proposto o desenvolvimento de um modelo de aprendizagem de máquina capaz de classificar determinados componentes ou a ausência deles em placas de circuito impresso SMD (Surface-Mount Device). Além da classificação, o sistema é capaz de inspecionar o posicionamento do componente, gerando um alerta para componentes rotacionados e deslocados. As SVMs (Máquinas de Vetores de Suporte) consistem na técnica de aprendizado de máquina utilizada na implementação do classificador. O modelo recebe como entrada um vetor de características que representa a forma, textura e cor das imagens dos componentes. As características de forma e textura são obtidas pelo HOG (Histograma de Gradientes Orientados) e a cor é caracterizada pelo histograma da imagem no espaço HSV (Hue, Saturation, Value). O modelo de classificação implementado alcançou uma acurácia de 98,7% nas imagens testadas.The quality inspection of final products in the Brazilian Industry is still manual or very little automated. Through the automation of this task, it is possible to increase productivity and product quality. To this end, this work aims to develop a printed circuit board automated inspection system. Therefore, a machine learning model capable of classifying SMD (Surface-Mount Device) components is proposed. In addition to the image classification, the system is able to inspect the component’s position, generating an alert for rotated and displaced components. SVMs (Support Vector Machines) are the machine learning technique used in the implementation of the classifier. The model receives as input a feature vector that contains information describing the shape, texture and color of the images. The shape and texture features are computed using HOG (Histogram of Oriented Gradients) and the color descriptor is created by calculating the histogram of the image in the HSV (Hue, Saturation and Value) color space. The model achieved an accuracy of 98.7% on the test images

Homogeneous vector capsules and their application to sufficient and complete data

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCapsules (vector-valued neurons) have recently become a more active area of research in neural networks. However, existing formulations have several drawbacks including the large number of trainable parameters that they require as well as the reliance on routing mechanisms between layers of capsules. The primary aim of this project is to demonstrate the benefits of a new formulation of capsules called Homogeneous Vector Capsules (HVCs) that overcome these drawbacks. Using HVCs, new state-of-the-art accuracies for the MNIST dataset are established for multiple individual models as well as multiple ensembles. This work additionally presents a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. Experiments performed and elucidated in this work examine classification accuracy of rotations and perspectives that were not trained on as well as the ability to artificially generate missing rotations and perspectives during training. The results of these experiments include showing that using HVCs is superior to using fully connected layers. This work also showed that certain training samples are more informative of class membership than others. These samples can be identified prior to training by analyzing their position in reduced dimensional space relative to the classes’ centroids in that space. And a definition and calculation both for class density and dataset completeness based on the distribution of data in the reduced dimensional space has been put forth. Experimentation using the dataset completeness calculation shows that those datasets that meet a certain completeness threshold can be trained on a subset of the total dataset, based on each class’s density, while improving upon or maintaining validation accuracy

Intelligent Robotic Recycling of Flat Panel Displays

As the population and prosperity continue to rise the demand for high-tech products is rapidly increasing. Displays are a huge part of this market with millions being created each year. We have a finite amount of resources on this planet and it is imperative that we properly dispose of our devices. In this thesis an intelligent robotic workcell for dismantling and recycling of flat panel displays is proposed and tested. This system utilizes industrial robots and open source tools to process a flat panel display (FPD) with the goal of removal of cold compact fluorescent tubes (CFLs). These are removed so the FPD can be shredded and properly recycled without the mercury present in the CFLs contaminating the materials or harming workers. This system utilizes many new and innovative techniques including deep learning algorithms for object detection and image segmentation. These deep learning techniques specifically Faster R-CNN for object detection and DeepLab for image segmentation have been shown to be extremely robust and capable in this application. The system was tested both with a real world system and with simulated geometry with a live vision feed. During simulation, the system was capable of processing flat panel FPDs in between 110 and 230 seconds per unit. It has been calculated using the simulation results, that this system can be profitable and has an approximate payback period of between 0.19 and 4.87 years depending on the material being fed into the system. The large range is due to the difference in value between monitor and TV style FPDs. The monitors have far less value than the TVs and are far more difficult to process