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Stress Analysis of printed circuit board with Different Thickness and Composite Materials Under Shock Loading

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Computer Modeling in Engineering & Sciences 2020年第2期122卷 661-674页

作者： Kuan-Ting Liu Chun-Lin Lu Nyan-Hwa Tai Meng-Kao Yeh Department of Power Mechanical Engineering National Tsing Hua UniversityHsinchu30013Taiwan. Department of Materials Science and Engineering National Tsing Hua UniversityHsinchu30013Taiwan.

In this study,the deformation and stress distribution of printed circuit board(PCB)with different thickness and composite materials under a shock loading were analyzed by the finite element *** standard 8-layer PCB subjected to a shock loading 1500 g was evaluated ***,the finite element models of the PCB with different thickness by stacking various number of layers were *** addition to changing thickness,the core material of PCB was replaced from woven E-glass/epoxy to woven carbon fiber/epoxy for structural *** non-linear material property of copper foil was considered in the *** results indicated that a thicker PCB has lower stress in the copper foil in PCBs under the shock *** stress difference between the thicker PCB(2.6 mm)and thinner PCB(0.6 mm)is around 5%.Using woven carbon fiber/epoxy as core material could lower the stress of copper foil around 6.6%under the shock loading 1500 g for the PCB with 0.6 mm *** the other hand,the stress level is under the failure strength of PCBs with carbon fiber/epoxy core layers and thickness 2.6 mm when the peak acceleration changes from 1500 g to 5000 *** study could provide a reference for the design and proper applications of the PCB with different thickness and composite materials.

关键词： Stress analysis printed circuit board composite material finite element analysis shock loading.

An Improved Randomized Circle Detection Algorithm Using in printed circuit board Locating Mark

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Applied Mathematics 2019年第10期10卷 848-861页

作者： Jingkun Liu Qi Fan Chengyi University College Jimei UniversityXiamenChina Xiamen Sinic-Tek Intelligent Technology Co. Ltd.XiamenChina

This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transform. The experimental results denote that this algorithm can locate the circular mark of printed circuit board (PCB).

关键词： Circle Detection Randomized Algorithm Characteristic of Circularity printed circuit board

Study on Pyrolysis Process for Recycling Waste printed circuit board

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Study on Pyrolysis Process for Recycling Waste Printed Circu...

Detection Algorithm of Surface Defect Word on printed circuit board

第八届东亚资源再生技术国际会议

作者： J. Guan M.X. Lu W.G Dong W.J. Tian and L. Zhou Institute of Chemical and Environmental Engineering China University of Mining and Technology (Beijing) Beijing 100083

＜正＞The pyrolysis process as an alternative method for recycling waste printed circuit board （PCB） has been studied. The properties of solid residue, liquid and gas in the process were investigated. Results show that this process is effective to recover valuable products and energy from PCB, and the layered residue can be easily liberated for metal recovery.

关键词： printed circuit board electronic scrap pyrolysis.

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Computer Systems Science & Engineering 2023年第9期46卷 3911-3923页

作者： Min Zhang Haixu Xi School of Computer Engineering Jiangsu University of TechnologyChangzhou213001China

For printed circuit board(PCB)surface defect detection,traditional detection methods mostly focus on template matching-based reference method and manual detections,which have the disadvantages of low defect detection efficiency,large errors in defect identification and localization,and low versatility of *** order to furthermeet the requirements of high detection accuracy,real-time and interactivity required by the PCB industry in actual production *** the current work,we improve the Youonly-look-once(YOLOv4)defect detection method to train and detect six types of PCB small target ***,the original Cross Stage Partial Darknet53(CSPDarknet53)backbone network is preserved for PCB defect feature information extraction,and secondly,the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection ***,the K-means++clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection,which further improves the recognition and localization of small PCB ***,the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class *** experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%,which has better detection capability,lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.

关键词： printed circuit board defect detection small target

Uncertain process-based data integration and residual lifetime evaluation of PCB in airborne equipment with ADT and field data

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Chinese Journal of Aeronautics 2024年第8期37卷 233-245页

作者： Yu WANG Rui KANG Linhan GUO Xiaoyang LI Zhe LIU Xiaohui WANG Weifang ZHANG Schoool of Reliability and Systems Engineering Beihang UniversityBeijing 100191China

Accurately evaluating the lifespan of the printed circuit board(PCB)in airborne equipment is an essential issue for aircraft design and operation in the marine atmospheric *** paper presents a novel evaluation method by fusing Accelerated Degradation Testing(ADT)data,degradation data,and life data of small samples based on the uncertainty degradation *** uncertain life model of PCB in airborne equipment is constructed by employing the uncertain distribution that considers the accelerated factor of multiple environmental conditions such as temperature,humidity,and *** addition,a degradation process model of PCB in airborne equipment is constructed by employing the uncertain process of fusing ADT data and field data,in which the performance characteristics of dynamic cumulative change are *** on minimizing the pth sample moments,an integrated method for parameter estimation of the PCB in airborne equipment is proposed by fusing the multi-source data of life,degradation,and *** engineering case illustrates the effectiveness and advantage of the proposed method.

关键词： printed circuit board Degradation Data integration Field environment Accelerated degradation testing Parameter estimate

Defect Detection in printed circuit boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

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Computers, Materials & Continua 2022年第1期70卷 637-652页

作者： Mohammed A.Alghassab Electrical Engineering Department College of EngineeringShaqra UniversityRiyadh11911Saudi Arabia

printed circuit boards(PCBs)are very important for proper functioning of any electronic *** are installed in almost all the electronic device and their functionality is dependent on the perfection of *** PCBs do not function properly then the whole electric machine might ***,keeping this in mind researchers are working in this field to develop error free *** these PCBs were examined by the human beings manually,but the human error did not give good results as sometime defected PCBs were categorized as ***,researchers and experts transformed this manual traditional examination to automated *** to this research image processing and computer vision came into actions where the computer vision experts applied image processing techniques to extract the ***,this also did not yield good ***,to further explore this area Machine Learning and Artificial Intelligence Techniques were *** this studywe have appliedDeep Neural Networks to detect the defects in the ***16and Inception networkswere applied to extract the relevant *** dataset was used in this study,it has 1500 pairs of both defected and non-defected *** pre-processing and data augmentation techniques were applied to increase the training *** neural networks were applied to classify the test *** results were compared with state-of-the art technique and it proved that the proposed methodology outperformed *** evaluation metrics were applied to evaluate the proposed *** 94.11%,Recall 89.23%,F-Measure 91.91%,and Accuracy 92.67%.

关键词： printed circuit board convolution neural network inception vgg16 data augmentation

A novel convolutional neural network for electronic component classification with diverse backgrounds

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第1期13卷 53-69页

作者： Longfei Zhou Lin Zhang Massachusetts Institute of Technology Cambridge MA 02139USA Beihang University Beijing 100191P.R.China

The rapid development of computer vision techniques has brought new opportunities for manufacturing industries,accelerating the intelligence of manufacturing systems in terms of product quality assurance,automatic assembly,and industrial robot *** the electronics manufacturing industry,intensive variability in component shapes and colors,background brightness,and visual contrast between components and back-ground results in difficulties in printed circuit board image *** this paper,we apply computer vision techniques to detect diverse electronic components from their background images,which is a challenging problem in electronics manufacturing indus-tries because there are multiple types of components mounted on the same printed circuit ***,a 13-layer convolutional neural network(ECON)is proposed to detect electronic components either of a single category or of diverse *** proposed network consists of five Convolution-MaxPooling blocks,followed by a flat-tened layer and two fully connected *** electronic component image dataset from a real manufacturing company is applied to compare the performance between ECON,Xception,VGG16,and *** this dataset,there are 11 categories of components as well as their background *** show that ECON has higher accuracy in both single-category and diverse component classification than the other networks.

关键词： printed circuit board surface-mount technology machine vision convolu-tional neural network classification

Analysis on the effectiveness of decoupling capacitor on mitigating radiated emission from power ground planes by BEM

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The Journal of China Universities of Posts and Telecommunications 2015年第1期22卷 77-82页

作者： Wen Shuhua Zhang Jinling Hu Yusheng School of Electronic Engineering Beijing University of Posts and Telecommunications College of Mechanical and Energy Engineering Jimei University

The radiation emission （RE） of multilayer primed circuit board （PCB） will cause electromagnetic compatibility problem. An efficient numerical method for evaluating the effectiveness of decoupling capacitors in reducing RE from power ground （P/G） planes in PCB was presented. A two-dimensional boundary-element method （BEM） was used to establish the radiation model. Then, the RE was calculated by equivalent magnetic current of edge field around P/G planes. Furthermore, the radiation and input impedance of P/G planes mounted decoupling capacitors were calculated for frequency up to 5 GHz The results were compared to those of the case without decoupling capacitors. It shows that the decoupling capacitors can effectively decrease RE except for anti-resonant frequency. At higher frequencies, the decoupling effectiveness mainly depends on the parasitic inductance of capacitor pin rather than the value.

关键词： printed circuit board radiation emission decoupling capacitors boundary-element method equivalent magnetic current

Fully Connected Convolutional Neural Network in PCB Soldering Point Inspection

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Journal of Computer and Communications 2022年第12期10卷 62-70页

作者： Bowen Cai Shanghai Xiashu Intelligent Science Company Co. Ltd. Shanghai China

In Electronics Manufacturing Services (EMS) industry, printed circuit board (PCB) inspection is tricky and hard, especially for soldering point inspection due to the extremely tiny size and inconsistent appearance for uneven heating in reflow soldering process. Conventional computer vision technique based on OpenCV or Halcon usually cause false positive call for originally good soldering point on PCB because OpenCV or Halcon use the pre-defined threshold in color proportion for deciding whether the specific soldering point is OK or NG (not good). However, soldering point forms are various after heating in reflow soldering process. This paper puts forward a VGG structure deep convolutional neural network, which is named SolderNet for processing soldering point after reflow heating process to effectively inspect soldering point status, reduce omission rate and error rate, and increase first pass rate. SolderNet consists of 11 hidden convolution layers and 3 densely connected layers. Accuracy reports are divided into OK point recognition and NG point recognition. For OK soldering point recognition, 92% is achieved. For NG soldering point recognition, 99% is achieved. The dataset is collected from KAGA Co. Ltd Plant in Suzhou. First pass rate at KAGA plant is increased from 25% to 80% in general.

关键词： Deep Learning Soldering Point Computer Vision Pattern Recognition Convolutional Neural Network printed circuit board Electronics Manufacturing Services