https://doi.org/10.26706/ijaefea.4.9.20221204

---

Abstract:-

The rapid advancement of Artificial Intelligence and Machine Learning has significantly accelerated the transformation of conventional manufacturing into smart manufacturing systems. These technologies enable data-driven decision-making, predictive analytics, and autonomous process control, thereby enhancing productivity, quality, and operational efficiency. This review paper aims to provide a comprehensive analysis of the application of AI and ML techniques in smart manufacturing, focusing on their roles in predictive maintenance, quality inspection, demand forecasting, process optimization, and intelligent supply chain management. A systematic review of recent literature has been conducted to examine various AI and ML models, including supervised and unsupervised learning algorithms, deep learning approaches, and hybrid intelligent systems. The findings reveal that AI and ML techniques significantly improve fault detection accuracy, reduce downtime, and enable real-time optimization of manufacturing processes. However, challenges such as data availability, model interpretability, computational requirements, and integration with legacy systems remain critical barriers to widespread adoption. This paper contributes by categorizing existing approaches, comparing their effectiveness, and identifying key research gaps for future investigation. The insights provided in this study can assist researchers and industry practitioners in selecting appropriate AI and ML techniques for the development of efficient and intelligent manufacturing systems.

Index Terms:-

Artificial Intelligence, Machine Learning, Smart Manufacturing, Predictive Maintenance, Process Optimization, Industry 4.0 .

---

REFERENCES

1. R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice, “Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions,” Sustainability, vol. 12, no. 2, p. 492, 2020.
2. K. P. Tran, Artificial intelligence for smart manufacturing: methods and applications. MDPI, 2021.
3. J. Wang, Y. Ma, L. Zhang, R. X. Gao, and D. Wu, “Deep learning for smart manufacturing: Methods and applications,” J. Manuf. Syst., vol. 48, pp. 144–156, 2018.
4. B. Bajic, I. Cosic, M. Lazarevic, N. Sremcev, and A. Rikalovic, “Machine learning techniques for smart manufacturing: Applications and challenges in Industry 4.0,” Dep. Ind. Eng. Manag., Novi Sad, Serbia, vol. 29, p. 18, 2018.
5. E. Balamurugan et al., “Use case of artificial intelligence in machine learning manufacturing 4.0,” in Proc. Int. Conf. Computational Intelligence and Knowledge Economy (ICCIKE), IEEE, 2019, pp. 656–659.
6. X. Yao, J. Zhou, J. Zhang, and C. R. Boër, “From intelligent manufacturing to smart manufacturing for Industry 4.0 driven by next generation artificial intelligence and further on,” in Proc. Int. Conf. Enterprise Systems (ES), IEEE, 2017, pp. 311–318.
7. M. Sharp, R. Ak, and T. Hedberg Jr, “A survey of the advancing use and development of machine learning in smart manufacturing,” J. Manuf. Syst., vol. 48, pp. 170–179, 2018.
8. Z. M. Çınar et al., “Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0,” Sustainability, vol. 12, no. 19, p. 8211, 2020.
9. R. Rai, M. K. Tiwari, D. Ivanov, and A. Dolgui, Machine learning in manufacturing and Industry 4.0 applications. Taylor & Francis, 2021.
10. A. Raj, “Artificial Intelligence and Machine Learning applications in Smart Production,” Artif. Intell., vol. 9, no. 6, 2021.
11. J. Wan et al., “Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges,” Proc. IEEE, vol. 109, no. 4, pp. 377–398, 2020.
12. B.-C. Hou et al., “Applications of artificial intelligence in intelligent manufacturing: a review,” Front. Inf. Technol. Electron. Eng., vol. 18, no. 1, pp. 86–96, 2017.
13. M. Woschank, E. Rauch, and H. Zsifkovits, “A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics,” Sustainability, vol. 12, no. 9, p. 3760, 2020.
14. Y. G. Kim et al., “Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system,” J. Manuf. Syst., vol. 57, pp. 440–450, 2020.
15. K. Alexopoulos, N. Nikolakis, and G. Chryssolouris, “Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing,” Int. J. Comput. Integr. Manuf., vol. 33, no. 5, pp. 429–439, 2020.
16. H. Ding et al., “State of AI-based monitoring in smart manufacturing and introduction to focused section,” IEEE/ASME Trans. Mechatronics, vol. 25, no. 5, pp. 2143–2154, 2020.
17. M. Andronie et al., “Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems,” Electronics, vol. 10, no. 20, p. 2497, 2021.
18. C.-F. Chien et al., Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies. Taylor & Francis, 2020.
19. J. Wan, J. Yang, Z. Wang, and Q. Hua, “Artificial intelligence for cloud-assisted smart factory,” IEEE Access, vol. 6, pp. 55419–55430, 2018.
20. T. Kotsiopoulos et al., “Machine learning and deep learning in smart manufacturing: The smart grid paradigm,” Comput. Sci. Rev., vol. 40, p. 100341, 2021.
21. K. Xia et al., “A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence,” J. Manuf. Syst., vol. 58, pp. 210–230, 2021.
22. Y. G. Hassan et al., “AI-driven intrusion detection and threat modeling to prevent unauthorized access in smart manufacturing networks,” Artif. Intell., vol. 16, 2021.
23. Y. Wang et al., “Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives,” Sci. China Technol. Sci., vol. 63, no. 9, pp. 1600–1611, 2020.
24. M. Ghahramani et al., “AI-based modeling and data-driven evaluation for smart manufacturing processes,” IEEE/CAA J. Autom. Sin., vol. 7, no. 4, pp. 1026–1037, 2020.
25. L. Zhou, L. Zhang, and B. K. P. Horn, “Deep reinforcement learning-based dynamic scheduling in smart manufacturing,” Procedia CIRP, vol. 93, pp. 383–388, 2020.
26. F. Tao, Q. Qi, A. Liu, and A. Kusiak, “Data-driven smart manufacturing,” J. Manuf. Syst., vol. 48, pp. 157–169, 2018.
27. S. Lee et al., “Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning,” PeerJ Comput. Sci., vol. 7, p. e350, 2021.
28. A. Essien and C. Giannetti, “A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders,” IEEE Trans. Ind. Informatics, vol. 16, no. 9, pp. 6069–6078, 2020.
29. D. Wu et al., “A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests,” J. Manuf. Sci. Eng., vol. 139, no. 7, p. 71018, 2017.
30. R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and A. Parmentola, “Smart manufacturing systems and applied industrial technologies for a sustainable industry: A systematic literature review,” Appl. Sci., vol. 10, no. 8, p. 2897, 2020.

---

To view full paper, Download here .

---