Engineering Innovation through Intelligent Systems: Case Studies and Future Directions
Keywords:
Engineering Innovation, Intelligent Systems, Artificial Intelligence, Machine Learning, Embedded Systems, Case Studies, Smart Engineering, Future TrendsAbstract
The use of intelligent systems in engineering has helped create new, adaptive and independent solutions for many industries. In this paper, I study how intelligent systems contribute to engineering innovation using various actual projects. It analyzes the effects of AI, ML and embedded systems on manufacturing, civil infrastructure, transportation and the energy industry. Studying these cases qualitatively and in comparison shows what trends, problems and opportunities exist in using intelligent technologies. Besides, the paper describes expanding directions, with emphasis on teamwork between different fields, ethics in AI and environment-friendly innovations in engineering. The information gained from Neural Engineering supports the application of intelligent tools in creating new developments in engineering.
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S. Phuyal, D. Bista, and R. Bista, “Challenges, Opportunities and Future Directions of Smart Manufacturing: A State of Art review,” Sustainable Futures, vol. 2, p. 100023, Jan. 2020, doi: 10.1016/j.sftr.2020.100023.
C. Zhang and J. Yang, A history of mechanical engineering. 2020. doi: 10.1007/978-981-15-0833-2.
K. Iranshahi, J. Brun, T. Arnold, T. Sergi, and U. C. Müller, “Digital Twins: recent advances and future directions in engineering fields,” Intelligent Systems With Applications, p. 200516, Apr. 2025, doi: 10.1016/j.iswa.2025.200516.
S. Aheleroff, X. Xu, R. Y. Zhong, and Y. Lu, “Digital Twin as a Service (DTAAS) in Industry 4.0: an architecture Reference model,” Advanced Engineering Informatics, vol. 47, p. 101225, Dec. 2020, doi: 10.1016/j.aei.2020.101225.
M. Almatared, H. Liu, S. Tang, M. Sulaiman, Z. Lei, and H. X. Li, “Digital twin in the architecture, engineering, and construction industry: A bibliometric review,” Construction Research Congress 2022, pp. 670–678, Mar. 2022, doi: 10.1061/9780784483961.070.
G. Bachelor, E. Brusa, D. Ferretto, and A. Mitschke, “Model-Based design of complex aeronautical systems through digital twin and thread concepts,” IEEE Systems Journal, vol. 14, no. 2, pp. 1568–1579, Sep. 2019, doi: 10.1109/jsyst.2019.2925627.
G. Bhatti, H. Mohan, and R. R. Singh, “Towards the future of smart electric vehicles: Digital twin technology,” Renewable and Sustainable Energy Reviews, vol. 141, p. 110801, Feb. 2021, doi: 10.1016/j.rser.2021.110801.
M. Deng, C. C. Menassa, and V. R. Kamat, “From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry,” Journal of Information Technology in Construction, vol. 26, pp. 58–83, Feb. 2021, doi: 10.36680/j.itcon.2021.005.
L. Deren, Y. Wenbo, and S. Zhenfeng, “Smart city based on digital twins,” Computational Urban Science, vol. 1, no. 1, Mar. 2021, doi: 10.1007/s43762-021-00005-y.
E. Azzaoui, T. W. Kim, V. Loia, and J. H. Park, “Blockchain-Based Secure Digital Twin Framework for Smart Healthy City,” in Lecture notes in electrical engineering, 2020, pp. 107–113. doi: 10.1007/978-981-15-9309-3_15.
M. N. K. Boulos and P. Zhang, “Digital Twins: From personalised medicine to precision public health,” Journal of Personalized Medicine, vol. 11, no. 8, p. 745, Jul. 2021, doi: 10.3390/jpm11080745.
M. Kor, I. Yitmen, and S. Alizadehsalehi, “An investigation for integration of deep learning and digital twins towards Construction 4.0,” Smart and Sustainable Built Environment, vol. 12, no. 3, pp. 461–487, Mar. 2022, doi: 10.1108/sasbe-08-2021-0148.
L. Li, S. Aslam, A. Wileman, and S. Perinpanayagam, “Digital twin in Aerospace industry: A gentle Introduction,” IEEE Access, vol. 10, pp. 9543–9562, Dec. 2021, doi: 10.1109/access.2021.3136458.
X. Liu et al., “A systematic review of digital twin about physical entities, virtual models, twin data, and applications,” Advanced Engineering Informatics, vol. 55, p. 101876, Jan. 2023, doi: 10.1016/j.aei.2023.101876.
O. C. Madubuike, C. J. Anumba, and R. Khallaf, “A review of digital twin applications in construction,” Journal of Information Technology in Construction, vol. 27, pp. 145–172, Feb. 2022, doi: 10.36680/j.itcon.2022.008.
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