Application of Machine Learning for Quality Risk Factor Analysis of Electronic Assemblies
Author:
Affiliation:
1. University of South Carolina,Department of Computer Science and Engineering,Columbia,SC,USA,29208
2. Juniper Networks,Component Engineering,Sunnyvale,CA,USA,94089
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10129281/10129282/10129339.pdf?arnumber=10129339
Reference20 articles.
1. Defect Classification of Printed Circuit Boards based on Transfer Learning
2. Defects and Components Recognition in Printed Circuit Boards Using Convolutional Neural Network
3. PCB-METAL: A PCB Image Dataset for Advanced Computer Vision Machine Learning Component Analysis
4. PCB Defect Detection Using Denoising Convolutional Autoencoders
5. Dropout: A simple way to prevent neural networks from overfitting;srivastava;Journal of Machine Learning Research,2014
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1. Predicting Defect Rates of Printed Circuit Board Assemblies: Towards Zero Defect Manufacturing and Zero-Maintenance Strategies;IFAC-PapersOnLine;2024
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