Machine Learning-Based Automation in Production Processes: Enhancing Efficiency and System Accuracy in Industry
DOI:
https://doi.org/10.62872/eqp79y32Keywords:
Machine Learning, Production Automation, Industrial Efficiency, Quality Control, Smart ManufacturingAbstract
The integration of Machine Learning (ML) in production automation has become a key driver in transforming industrial systems into smart and adaptive manufacturing environments. This study aims to analyze the role of ML in improving efficiency and accuracy within production processes. The research employs a qualitative approach with a descriptive-analytical design, using library research and document analysis of reputable scientific sources. Data were analyzed through an interactive model consisting of data reduction, data display, and conclusion drawing. The findings reveal that ML significantly enhances operational efficiency through predictive maintenance, optimized scheduling, and real-time decision-making, while also improving accuracy in quality control through advanced algorithms such as deep learning, Support Vector Machines, and Artificial Neural Networks. Furthermore, ML enables process optimization by analyzing complex production variables and identifying optimal parameters. However, challenges such as data quality, system integration, and model interpretability remain critical barriers. The study concludes that a holistic integration of ML, supported by advanced technologies such as IIoT and Digital Twin, is essential for achieving higher efficiency, improved accuracy, and sustainable competitiveness in modern industrial systems.
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References
(2024). Leveraging topological data analysis and AI for advanced manufacturing: Integrating machine learning and automation for predictive maintenance and process optimization. International Journal of Computer Applications Technology and Research. https://doi.org/10.7753/10.7753/ijcatr1309.1003
Cruz, Y., Villalonga, A., Castaño, F., Rivas, M., & Haber, R. (2023). Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4334026
Cruz, Y., Villalonga, A., Castaño, F., Rivas, M., & Haber, R. (2024). Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises. Operations Research Perspectives. https://doi.org/10.1016/j.orp.2024.100308
Galindo-Salcedo, M., Pertúz-Moreno, A., Guzmán-Castillo, S., Gómez-Charris, Y., & Romero-Conrado, A. (2022). Smart manufacturing applications for inspection and quality assurance processes. Procedia Computer Science, 536–541. https://doi.org/10.1016/j.procs.2021.12.282
Johanesa, T., Equeter, L., & Mahmoudi, S. (2024). Survey on AI applications for product quality control and predictive maintenance in Industry 4.0. Electronics. https://doi.org/10.3390/electronics13050976
Kausik, A., Rashid, A., Baki, R., & Maktum, M. (2025). Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications. Array, 26, 100393. https://doi.org/10.1016/j.array.2025.100393
Maguluri, L., Suganthi, D., Dhote, G., Kapila, D., Jadhav, M., & Neelima, S. (2024). AI-enhanced predictive maintenance in hybrid roll-to-roll manufacturing integrating multi-sensor data and self-supervised learning. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-024-14263-7
Manivannan, S. (2022). Automatic quality inspection in additive manufacturing using semi-supervised deep learning. Journal of Intelligent Manufacturing, 34, 3091–3108. https://doi.org/10.1007/s10845-022-02000-4
Mayer, J., & Jochem, R. (2024). Capability indices for digitized industries: A review and outlook of machine learning applications for predictive process control. Processes. https://doi.org/10.3390/pr12081730
Mazzei, D., & Ramjattan, R. (2022). Machine learning for Industry 4.0: A systematic review using deep learning-based topic modelling. Sensors (Basel, Switzerland), 22. https://doi.org/10.3390/s22228641
Phan, T., Gehrhardt, I., Heik, D., Bahrpeyma, F., & Reichelt, D. (2022). A systematic mapping study on machine learning techniques applied for condition monitoring and predictive maintenance in the manufacturing sector. Logistics. https://doi.org/10.3390/logistics6020035
Rahman, M., Shahrior, M., Iqbal, K., & Abushaiba, A. (2025). Enabling intelligent industrial automation: A review of machine learning applications with digital twin and edge AI integration. Automation. https://doi.org/10.3390/automation6030037
Rai, R., Tiwari, M., & Dolgui, A. (2021). Machine learning in manufacturing and Industry 4.0 applications. International Journal of Production Research, 59, 4773–4778. https://doi.org/10.1080/00207543.2021.1956675
Shafiq, M., Thakre, K., Krishna, K., Robert, N., Kuruppath, A., & Kumar, D. (2023). Continuous quality control evaluation during manufacturing using supervised learning algorithm for Industry 4.0. The International Journal of Advanced Manufacturing Technology, 1–10. https://doi.org/10.1007/s00170-023-10847-x
Shahrani, A., Alomar, M., Alqahtani, K., Basingab, M., Sharma, B., & Rizwan, A. (2022). Machine learning-enabled smart industrial automation systems using Internet of Things. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23010324
Tercan, H., & Meisen, T. (2022). Machine learning and deep learning-based predictive quality in manufacturing: A systematic review. Journal of Intelligent Manufacturing, 33, 1879–1905. https://doi.org/10.1007/s10845-022-01963-8
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, 107864. https://doi.org/10.1016/j.ress.2021.107864
Wang, X., Hu, H., Wang, Y., & Wang, Z. (2024). IoT real-time production monitoring and automated process transformation in smart manufacturing. Journal of Organizational and End User Computing, 36, 1–25. https://doi.org/10.4018/joeuc.336482
Yao, Y., & Qian, Q. (2024). Dynamic industrial optimization: A framework integrates online machine learning for processing parameters design. Future Internet, 16, 94. https://doi.org/10.3390/fi16030094
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Copyright (c) 2026 Amali Amali, Amalia Tasya (Author)
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