FUSION AI: A Knowledge-Driven AI Employee for Engineering Problem Solving in Tyre Manufacturing

FUSION AI presents a practical framework for applying AI to engineering problem-solving in tyre manufacturing. The proposed system combines retrieval-augmented generation with an engineering knowledge base and established methods such as RCA and FMEA [1], [9], [15]. Its main purpose is to reduce the time engineers spend searching for technical information and preparing investigation reports while retaining engineering review and validation. The framework also provides a way to retain knowledge from previous investigations and make it available for future cases, addressing a common challenge in manufacturing knowledge management [6], [7]. The proposed integration with IoT, predictive maintenance, computer vision, MES, ERP, and digital twins provides scope for further development. The performance and financial improvements presented in the demonstration are illustrative and require validation using real industrial data. Overall, FUSION AI is best considered a human-centred engineering support framework that can make existing manufacturing knowledge easier to access, apply, and preserve. This aligns with the broader objectives of Industry 5.0, where technology is intended to strengthen human capabilities within industrial systems [13], [14].

1. P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems, vol. 33, 2020.   2. S. Wu, Y. Xiong, Y. Cui, H. Wu, C. Chen, Y. Yuan, L. Huang, X. Liu, T.-W. Kuo, and C. J. Xue, “Retrieval-Augmented Generation for Natural Language Processing: A Survey,” 2024.   3. C. Chen, L. Li, T. Wang, Z. Wang, J. Leng, H. Shao, and L. Cheng, “Leveraging large language models for smart manufacturing: Reviews, enablers, challenges, and opportunities,” Journal of Manufacturing Systems, vol. 85, pp. 593–612, 2026.   4. “Large language models for fault diagnosis in smart manufacturing: A comprehensive review,” Intelligent Systems with Applications, vol. 31, 200714, 2026.   5. Y. Wan, Z. Chen, Y. Liu, C. Chen, and M. Packianather, “Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing,” Advanced Engineering Informatics, vol. 65, 103212, 2025.   6. “Knowledge Management 4.0: Theoretical and Practical Considerations in Cyber Physical Production Systems,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 1597–1602, 2019.   7. Y. Wan, Y. Liu, Z. Chen, C. Chen, X. Li, F. Hu, and M. Packianather, “Making knowledge graphs work for smart manufacturing: Research topics, applications and prospects,” Journal of Manufacturing Systems, vol. 76, pp. 103–132, 2024.   8. Y. Xiao, S. Zheng, J. Shi, X. Du, and J. Hong, “Knowledge graph-based manufacturing process planning: A state-of-the-art review,” Journal of Manufacturing Systems, vol. 70, pp. 417–435, 2023.   9. D. Pietsch, M. Matthes, U. Wieland, S. Ihlenfeldt, and T. Munkelt, “Root Cause Analysis in Industrial Manufacturing: A Scoping Review of Current Research, Challenges and the Promises of AI-Driven Approaches,” Journal of Manufacturing and Materials Processing, vol. 8, no. 6, p. 277, 2024.   10. T. Zonta, C. A. da Costa, R. da Rosa Righi, M. J. de Lima, E. S. da Trindade, and G. P. Li, “Predictive maintenance in the Industry 4.0: A systematic literature review,” Computers & Industrial Engineering, vol. 150, 106889, 2020.