Research on AI optimization of intelligent manufacturing strategies to enhance shipbuilding efficiency
Yihe Zhao
Shanghai Maritime University
DOI: https://doi.org/10.59429/esta.v12i1.9647
Keywords: Shipbuilding; Artificial intelligence; Intelligent manufacturing; Predictive maintenance; Digital twin technology
Abstract
Shipbuilding is a complex and resource-intensive industry that requires high efficiency and precision. With the advancement of artificial intelligence (AI) and intelligent manufacturing technologies, there is a growing opportunity to optimize shipbuilding strategies. This paper explores the application of AI in enhancing shipbuilding efficiency through intelligent manufacturing, focusing on key areas such as predictive maintenance, process automation, quality control, and supply chain optimization. By integrating AI-driven techniques such as machine learning, computer vision, and digital twin technology, the shipbuilding industry can significantly reduce costs, improve production accuracy, and accelerate delivery times. This research provides an overview of existing AI methodologies, discusses their potential benefits, and outlines the challenges and future directions of AI-optimized intelligent manufacturing in shipbuilding.
References
[1] Shah, R., & Ward, P. T. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of operations management, 21(2), 129-149.
[2] Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111.
[3] Tien, J. M. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4, 149-178.
[4] Abbas, A. (2024). AI for predictive maintenance in industrial systems. International Journal of Advanced Engineering Technologies and Innovations, 1(1), 31-51.
[5] Yoo, S., Reza, S., Tarashiyoun, H., Ajikumar, A., & Moghaddam, M. (2024). AI-Integrated AR as an Intelligent Companion for Industrial Workers: A Systematic Review. IEEE Access.
[6] Taheri, H., & Salimi Beni, A. (2025). Artificial Intelligence, Machine Learning, and Smart Technologies for Nondestructive Evaluation. Handbook of Nondestructive Evaluation 4.0, 1-29.
[7] Ikpogu, N. M. (2021). Barriers to technology adoption among maritime industry stakeholders in Nigeria (Doctoral dissertation, Walden University).
[8] Roh, Y., Heo, G., & Whang, S. E. (2019). A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1328-1347.