Received 03.04.2025, Revised 24.06.2025, Accepted 28.08.2025

Application of deep neural networks to automate production quality control in real time

Vitalii Yasenenko

The purpose of the study was to investigate the impact of using deep neural networks on improving the efficiency of automated quality control in contemporary production. The analysis of the application of advanced technologies, such as computer vision, machine learning, and deep neural networks, to automate quality control processes in production conditions was carried out. Successful implementations of automated quality control systems at enterprises such as Bayerische Motoren Werke AG, Siemens, and Nikon were considered as practical examples. The data obtained confirmed that the use of convolutional neural networks for image and video processing, autoencoders, and generative conflicting networks provides high accuracy and speed in detecting defects. In particular, an increase in the accuracy of defect identification was recorded from 80% to 95%, completeness – from 85% to 92%, specificity – from 90% to 98%. The speed of image and video processing increased fivefold – from 5 minutes to 1 minute per unit of production, which significantly reduced the control cycle time. Improving the defect detection rate to 95% helped to reduce the cost of manual verification and minimise the impact of the human factor. The conducted comparative analysis confirmed that automated systems based on deep neural networks significantly outperform conventional methods of monitoring key performance metrics. The study also showed that the integration of such systems with data processing peripherals and cloud platforms provides high flexibility and scalability of production processes. The economic assessment showed a significant reduction in labour costs and the number of errors, and an increase in the overall productivity of enterprises using automated quality control systems. The results obtained can be used as practical recommendations for companies interested in implementing innovative approaches to product quality assurance

intelligent monitoring of production quality; data processing speed; machine learning; defect detection; computer vision; autoencoders
157-169
Yasenenko, V. (2025). Application of deep neural networks to automate production quality control in real time. Information Technologies and Computer Engineering, 22(2), 157-169. https://doi.org/10.31649/vitce/2.2025.157

References

[1] Ahmed, I., Ahmad, M., Chehri, A., & Jeon, G. (2023). A smart-anomaly-detection system for industrial machines based on feature autoencoder and deep learning. Micromachines, 14(1), article number 154. doi: 10.3390/ mi14010154.

[2] Banadaki, Y., Razaviarab, N., Fekrmandi, H., Li, G., Mensah, P., Bai, S., & Sharifi, S. (2021). Automated quality and process control for additive manufacturing using deep convolutional neural networks. Recent Progress in Materials, 4(1). doi: 10.21926/rpm.2201005.

[3] Belytskyi, D., Yermolenko, R., Petrenko, K., & Gogota, O. (2023). Application of machine learning and computer vision methods to determine the size of NPP equipment elements in difficult measurement conditions. Machinery & Energetics, 14(4), 42-53. doi: 10.31548/machinery/4.2023.42.

[4] Bondarchuk, A.P., Oleinikov, I.A., & Bazhan, T.O. (2024). Application of machine learning methods to 3D printer control. Telecommunication and Information Technologies, 82(1), 4-15. doi: 10.31673/2412-4338.2024.010415.

[5] Borovyk, D.O. (2024). Deep learning information technology for detecting prohibited items during customs control and customs clearance. Sumy: Sumy State University.

[6] Chouhad, H., El Mansori, M., Knoblauch, R., & Corleto, C. (2021). Smart data driven defect detection method for surface quality control in manufacturing. Measurement Science and Technology, 32(10), article number 105403. doi: 10.1088/1361-6501/ac0b6c.

[7] Cumbajin, E., Rodrigues, N., Costa, P., Miragaia, R., Frazão, L., Costa, N., Fernández-Caballero, A., Carniero, J., Buruberri, L.H., & Pereira, A. (2023). A real-time automated defect detection system for ceramic pieces manufacturing process based on computer vision with deep learning. Sensors, 24(1), article number 232. doi: 10.3390/s24010232.

[8] Dorafshan, S., Thomas, R.J., Coopmans, C., & Maguire, M. (2018). Deep learning neural networks for sUAS-assisted structural inspections: Feasibility and application. In Proceedings of the international conference on unmanned aircraft systems (pp. 874-882). Dallas: IEEE. doi: 10.1109/ICUAS.2018.8453409.

[9] Ghojogh, B., Ghodsi, A., Karray, F., & Crowley, M. (2021). Generative adversarial networks and adversarial autoencoders: Tutorial and survey. ArXiv. doi: 10.48550/arXiv.2111.13282.

[10] Hernández-García, A., & König, P. (2018). Further advantages of data augmentation on convolutional neural networks. In V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis & I. Maglogiannis (Eds.), Artificial neural networks and machine learning (pp. 95-103). Cham: Springer. doi: 10.1007/978-3-030-01418-6_10.

[11] Islam, M.R., Zamil, M.Z., Rayed, M.E., Kabir, M.M., Mridha, M.F., Nishimura, S., & Shin, J. (2024). Deep learning and computer vision techniques for enhanced quality control in manufacturing processes. IEEE Access, 12, 121449-121479. doi: 10.1109/ACCESS.2024.3453664.

[12] Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999-7019. doi: 10.1109/ TNNLS.2021.3084827.

[13] Lu, Y., Xu, X., & Wang, L. (2020). Smart manufacturing process and system automation – a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312-325. doi: 10.1016/j.jmsy.2020.06.010.

[14] Mienye, I.D., & Swart, T.G. (2025). Deep autoencoder neural networks: A comprehensive review and new perspectives. Archives of Computational Methods in Engineering. doi: 10.1007/s11831-025-10260-5.

[15] Misra, P., & Tiwari, N. (2022). The role of machine vision technology in the manufacturing of vehicles in industry. Turkish Journal of Computer and Mathematics Education, 13(3), 778-798. doi: 10.17762/turcomat.v13i03.13149.

[16] Njeri, N.R. (2022). Data preparation for machine learning modelling. International Journal of Computer Applications Technology and Research, 11(6), 231-235. doi: 10.7753/IJCATR1106.1008

[17] Prymyska, S., Abramova, A., & Skladannyj, D. (2025). Integration of artificial intelligence into industrial process automation systems. Computer-Integrated Technologies: Education, Science, Production, 58, 12-20. doi: 10.36910/67752524-0560-2025-58-02.

[18] Ramamoorthi, V. (2023). Applications of AI in cloud computing: Transforming industries and future opportunities. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(4), 472-483. doi: 10.32628/CSEIT2390910.

[19] Rosca, C.M., R dulescu, G., & Stancu, A. (2025). Artificial intelligence of things infrastructure for quality control in cast manufacturing environments shedding light on industry changes. Applied Sciences, 15(4), article number 2068. doi: 10.3390/app15042068.

[20] Roy, Q., Zhang, F., & Vogel, D. (2019). Automation accuracy is good, but high controllability may be better. In S. Brewster & G. Fitzpatrick (Eds.), Proceedings of the 2019 CHI conference on human factors in computing systems (article number 520). New York: Association for Computing Machinery. doi: 10.1145/3290605.3300750.

[21] Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using machine learning and edge cloud computing. Advanced Engineering Informatics, 45, article number 101101.doi: 10.1016/j.aei.2020.101101.

[22] Shahin, M., Maghanaki, M., Hosseinzadeh, A., & Chen, F.F. (2024). Improving operations through a lean AI paradigm: A view to an AI-aided lean manufacturing via versatile convolutional neural network. International Journal of Advanced Manufacturing Technology, 133(11), 5343-5419. doi: 10.1007/s00170-024-13874-4.

[23] Singh, R., & Gill, S.S. (2023). Edge AI: A survey. Internet of Things and Cyber-Physical Systems, 3, 71-92. doi: 10.1016/j. iotcps.2023.02.004.

[24] Singh, S.A., & Desai, K.A. (2023). Automated surface defect detection framework using machine vision and convolutional neural networks. Journal of Intelligent Manufacturing, 34(4), 1995-2011. doi: 10.1007/s10845-02101878-w.

[25] Sundaram, S., & Zeid, A. (2023). Artificial intelligence-based smart quality inspection for manufacturing. Micromachines, 14(3), article number 570. doi: 10.3390/mi14030570.

[26] Thakur, R., Panghal, D., Jana, P., Rajan, & Prasad, A. (2023). Automated fabric inspection through convolutional neural network: An approach. Neural Computing and Applications, 35(5), 3805-3823. doi: 10.1007/s00521-022-07891-1.

[27] Villalba-Diez, J., Schmidt, D., Gevers, R., Ordieres-Meré, J., Buchwitz, M., & Wellbrock, W. (2019). Deep learning for industrial computer vision quality control in the printing industry 4.0. Sensors, 19(18), article number 3987. doi: 10.3390/s19183987.

[28] Wang, Y., Perry, M., Whitlock, D., & Sutherland, J.W. (2022). Detecting anomalies in time series data from a manufacturing system using recurrent neural networks. Journal of Manufacturing Systems, 62, 823-834. doi: 10.1016/j. jmsy.2020.12.007.

[29] Zipfel, J., Verworner, F., Fischer, M., Wieland, U., Kraus, M., & Zschech, P. (2023). Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models. Computers & Industrial Engineering, 177, article number 109045. doi: 10.1016/j.cie.2023.109045.