Circuit Manufacturing Defect Detection Using VGG16 Convolutional Neural Networks
Authors: 
Sara A. Althubiti, Fayadh Alenezi, S. Shitharth, Sangeetha K., Chennareddy Vijay Simha Reddy Academic Editor: Kuruva LakshmannaAuthors Info & Claims
Sara A. Althubiti, Fayadh Alenezi, S. Shitharth, Sangeetha K., Chennareddy Vijay Simha Reddy Academic Editor: Kuruva LakshmannaAuthors Info & ClaimsAbstract
Manufacturing, one of the most valuable industries in the world, is boundlessly automatable yet still quite stuck in traditionally manual and slow processes. Industry 4.0 is racing to define a new era of digital manufacturing through Internet of Things- (IoT-) connected machines and factory systems, fully comprehensive data gathering, and seamless implementation of data-driven decision-making and action taking. Both academia and industry understand the tremendous value in modernizing manufacturing and are pioneering bleeding-edge strides every day to optimize one of the largest industries in the world. IoT production, functional testing, and fault detection equipment are already being used in today’s maturing smart factory paradigm to superintend intelligent manufacturing equipment and perform automated defect detection in order to enhance production quality and efficiency. This paper presents a powerful and precise computer vision model for automated classification of defect product from standard product. Human operators and inspectors without digital aid must spend inordinate amounts of time poring over visual data, especially in high volume production environments. Our model works quickly and accurately in sparing defective product from entering doomed operations that would otherwise incur waste in the form of wasted worker-hours, tardy disposition, and field failure. We use a convolutional neural network (CNN) with the Visual Geometry Group with 16 layers (VGG16) architecture and train it on the Printed Circuit Board (PCB) dataset with 3175 RBG images. The resultant trained model, assisted by finely tuned optimizers and learning rates, classifies defective product with 97.01% validating accuracy.
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Copyright © 2022 Sara A. Althubiti et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Published: 01 January 2022
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