Automated Defect Detection in Manufacturing Using Enhanced VGG16 Convolutional Neural Networks

Baiganova A. Ubayeva Z. Taskalyeva Z. Kaparova L. Nurzhaubaeva R. Umirzakova B.
2025 Science and Information Organization

International Journal of Advanced Computer Science and Applications
2025 #16 Issue 4 877 - 888 pp.

Automated defect detection in manufacturing is a critical component of modern quality control, ensuring high production efficiency and minimizing defective outputs. This study presents an enhanced VGG16-based convolutional neural network (CNN) model for defect classification and localization, improving upon traditional vision-based inspection methods. The proposed model integrates advanced deep learning techniques, including batch normalization and dropout regularization, to enhance generalization and prevent overfitting. Extensive experiments were conducted on benchmark manufacturing defect datasets, evaluating performance based on accuracy, loss evolution, precision, recall, and mean average precision (mAP). The results demonstrate that the enhanced VGG16 model outperforms conventional CNN architectures and the standard VGG16, achieving higher defect classification accuracy and superior feature extraction capabilities. The model successfully detects multiple defect types, including surface irregularities, scratches, and deformations, with improved robustness in complex industrial environments. Additionally, the receiver operating characteristic (ROC) analysis confirms the model’s high sensitivity and specificity in distinguishing between defective and non-defective components. Despite its strong performance, challenges such as dataset scarcity, computational costs, and model interpretability remain areas for further research. Future directions include the integration of lightweight architectures for real-time deployment, generative adversarial networks (GANs) for data augmentation, and explainable AI techniques for improved transparency. The findings of this study highlight the transformative potential of deep learning in manufacturing defect detection, paving the way for intelligent, automated quality control systems that enhance production efficiency and reliability. The proposed approach contributes to the advancement of Industry 4.0 by enabling scalable, data-driven decision-making in manufacturing processes.

Automated defect detection , convolutional neural networks , deep learning , Industry 4.0 , machine vision , manufacturing inspection , quality control , VGG16

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K. Zhubanov Aktobe Regional University, Aktobe, Kazakhstan

K. Zhubanov Aktobe Regional University

10 лет помогаем публиковать статьи Международный издатель

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