INTELLIGENT PRINT QUALITY CONTROL USING CNN MODELS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6813Keywords:
Print Quality Control, Convolutional Neural Network (CNN), Defect Detection, Transfer Learning, Real-Time Inspection, Image ProcessingAbstract [English]
Controlling the quality of prints is a vital process in the current printing industries so as to maintain the quality of the mass production in terms of consistency, accuracy, and aesthetic features. The typical machine-vision systems find it difficult to detect the invisible defects like streaks, blotches, misalignment and color differences because of shortcomings of hand-made extraction of features. The proposed research is an intelligent print quality control system based on the Convolutional Neural Network (CNN) models to detect and classify defects automatically. The method starts with the generation of a set of complete dataset which consists of high-resolution images of controlled light and camera conditions. All images are annotated and labelled based on certain types of defects in order to enable supervised learning. The preprocessing of the data, such as normalization, resizing, augmentation, color enhancement, etc. are used to enhance model robustness and generalization. Various CNNs, including VGG, ResNet and MobileNet, are studied using the method of transfer learning to maximize the accuracy and performance. The CNN model created to be customized comprises of other layers as well covering localization of defect and region based detection. The real-time image capture, CNN inference and decision-making modules are incorporated into the system architecture, and executed at the edge computing devices or on the GPU, to offer them fast processing. The classification accuracy and real-time processing results suggest that the experimental results are better than the traditional vision-based systems.
References
Ali, Y., Shah, S. W., Khan, W. A., and Waqas, M. (2022). Cyber Secured Internet of Things-Enabled Additive Manufacturing: Industry 4.0 Perspective. Journal of Advanced Manufacturing Systems, 22(2), 239–255. https://doi.org/10.1142/S0219686723500129 DOI: https://doi.org/10.1142/S0219686723500129
Bode, G., Thul, S., Baranski, M., and Müller, D. (2020). Real-World Application of Machine-Learning-Based Fault Detection Trained with Experimental Data. Energy, 198, 117323. https://doi.org/10.1016/j.energy.2020.117323 DOI: https://doi.org/10.1016/j.energy.2020.117323
Chen, Q., Yao, Y., Gui, G., and Yang, S. (2022). Gear Fault Diagnosis Under Variable Load Conditions Based on Acoustic Signals. IEEE Sensors Journal, 22(22), 22344–22355. https://doi.org/10.1109/JSEN.2022.3214286 DOI: https://doi.org/10.1109/JSEN.2022.3214286
Cui, P. H., Wang, J. Q., and Li, Y. (2022). Data-Driven Modelling, Analysis and Improvement of Multistage Production Systems with Predictive Maintenance and Product Quality. International Journal of Production Research, 60(21), 6848–6865. https://doi.org/10.1080/00207543.2021.1962558 DOI: https://doi.org/10.1080/00207543.2021.1962558
Farhat, M. H., Hentati, T., Chiementin, X., Bolaers, F., Chaari, F., and Haddar, M. (2023). Numerical Model of a Single Stage Gearbox Under Variable Regime. Mechanics Based Design of Structures and Machines, 51(5), 1054–1081. https://doi.org/10.1080/15397734.2020.1863226 DOI: https://doi.org/10.1080/15397734.2020.1863226
Gehrmann, C., and Gunnarsson, M. (2020). A Digital Twin-Based Industrial Automation and Control System Security Architecture. IEEE Transactions on Industrial Informatics, 16(1), 669–680. https://doi.org/10.1109/TII.2019.2938885 DOI: https://doi.org/10.1109/TII.2019.2938885
Ghobakhloo, M. (2020). Industry 4.0, Digitization, and Opportunities for Sustainability. Journal of Cleaner Production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869 DOI: https://doi.org/10.1016/j.jclepro.2019.119869
Groumpos, P. P. (2021). A Critical Historical and Scientific Overview of All Industrial Revolutions. IFAC-PapersOnLine, 54(13), 464–471. https://doi.org/10.1016/j.ifacol.2021.10.492 DOI: https://doi.org/10.1016/j.ifacol.2021.10.492
Gundewar, S. K., and Kane, P. V. (2021). Condition Monitoring and Fault Diagnosis of Induction Motor. Journal of Vibration Engineering and Technologies, 9(4), 643–674. https://doi.org/10.1007/s42417-020-00253-y DOI: https://doi.org/10.1007/s42417-020-00253-y
Patel, A., and Shakya, P. (2021). Spur Gear Crack Modelling and Analysis Under Variable Speed Conditions using Variational Mode Decomposition. Mechanism and Machine Theory, 164, 104357. https://doi.org/10.1016/j.mechmachtheory.2021.104357 DOI: https://doi.org/10.1016/j.mechmachtheory.2021.104357
Ren, Z., Fang, F., Yan, N., and Wu, Y. (2022). State of the Art in Defect Detection Based on Machine Vision. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(3), 661–691. https://doi.org/10.1007/s40684-021-00343-6 DOI: https://doi.org/10.1007/s40684-021-00343-6
Schmidl, S., Wenig, P., and Papenbrock, T. (2022). Anomaly Detection in Time Series: A Comprehensive Evaluation. Proceedings of the VLDB Endowment, 15(8), 1779–1797. https://doi.org/10.14778/3538598.3538602 DOI: https://doi.org/10.14778/3538598.3538602
Xu, M., David, J. M., and Kim, S. H. (2018). The Fourth Industrial Revolution: Opportunities and Challenges. International Journal of Financial Research, 9(2), 90–95. https://doi.org/10.5430/ijfr.v9n2p90 DOI: https://doi.org/10.5430/ijfr.v9n2p90
Yu, L., Yao, X., Yang, J., and Li, C. (2020). Gear Fault Diagnosis Through Vibration and Acoustic Signal Combination Based on Convolutional Neural Network. Information, 11(5), 266. https://doi.org/10.3390/info11050266 DOI: https://doi.org/10.3390/info11050266
Zhang, S., Zhou, J., Wang, E., Zhang, H., Gu, M., and Pirttikangas, S. (2022). State of the Art on Vibration Signal Processing Towards Data-Driven Gear Fault Diagnosis. IET Collaborative Intelligent Manufacturing, 4(3), 249–266. https://doi.org/10.1049/cim2.12064 DOI: https://doi.org/10.1049/cim2.12064
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Vaibhav Kaushik, Kajal Thakuriya, Paul Praveen Albert Selvakumar, Jatin Khurana, Shrushti Deshmukh, Saritha SR
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
