Integration of digital twins and artificial intelligence for sustainable mechanical component design
Arangarajan M, Lokanayaki KARNAN, R. KAVITHA, Sonali GUPTA, Syed Hassan Imam GARDEZI, Surekha S. S. S
Abstract. Purpose: The fast development of cyber-physical systems has led to new opportunities in improving the sustainability of mechanical components design. This article suggests a combined system that interconnects Digital Twin (DT) technology with Artificial Intelligence (AI) to allow predictive optimisation and sustainable design of mechanical parts. Methodology: A deep neural network (DNN) is trained using data of high-fidelity finite element simulations which are synchronized with real-time sensor measurements of a physical component testbed. Multi-objective optimisation, which aims to minimise structural mass, material consumption and lifecycle carbon emissions through reinforcement learning (RL), is also utilized. Findings: The DT-DNN model used has a normalised RMSE of 0.043 with a R 2 of 0.958, which is 70.9% more accurate than the base FEA model. The DT-RL optimiser results in 24.3 per cent mass reduction and 22.1 per cent carbon saving compared to the traditional design practices. Conclusion: The results indicate that the symbiotic approach of digital twins and AI is a strong, scalable strategy to continue the evolution of sustainable mechanical engineering practice.
Keywords
Digital Twins, Artificial Intelligence, Sustainable Design, Deep Neural Networks, Reinforcement Learning, Mechanical Components, Multi-Objective Optimisation
Published online 8/15/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Arangarajan M, Lokanayaki KARNAN, R. KAVITHA, Sonali GUPTA, Syed Hassan Imam GARDEZI, Surekha S. S. S, Integration of digital twins and artificial intelligence for sustainable mechanical component design, Materials Research Proceedings, Vol. 71, pp 25-32, 2026
DOI: https://doi.org/10.21741/97816449042312-4
The article was published as article 4 of the book AI-Driven Innovations in Mechanical, Manufacturing, and Energy Engineering
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] S. Dafermos and M. Florian, “The Traffic Assignment Problem for Multiclass-User Transportation Networks,” IEEE Trans. Ind. Electron., vol. 70, no. 3, pp. 2341-2352, Mar. 2023.
[2] M. Grieves, “Digital Twin: Manufacturing Excellence Through Virtual Factory Replication,” White Paper, Florida Inst. Technol., Melbourne, FL, USA, 2014.
[3] P. Davim, Ed., Sustainable Manufacturing, 2nd ed. London, U.K.: ISTE-Wiley, 2020.
[4] F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, “Digital Twin in Industry: State-of-the-Art,” IEEE Trans. Ind. Inform., vol. 15, no. 4, pp. 2405-2415, Apr. 2019. https://doi.org/10.1109/TII.2018.2873186
[5] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on Complex Systems, F.-J. Kahlen, S. Flumerfelt, and A. Alves, Eds. Cham, Switzerland: Springer, 2017, pp. 85-113. https://doi.org/10.1007/978-3-319-38756-7_4
[6] E. Glaessgen and D. Stargel, “The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles,” in Proc. 53rd AIAA/ASME/ASCE/AHS/ASC Struct., Struct. Dyn. Mater. Conf., Honolulu, HI, USA, Apr. 2012, pp. 1-14. https://doi.org/10.2514/6.2012-1818
[7] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436-444, May 2015. https://doi.org/10.1038/nature14539
[8] F. Tao, W. Liu, J. Liu, X. Liu, Q. Liu, and W. Qu, “Digital Twin and Its Potential Application Exploration,” Comput. Integr. Manuf. Syst., vol. 24, no. 1, pp. 1-18, Jan. 2018.
[9] Z. Chen, S. Deng, X. Chen, C. Li, R. Sanchez, and H. Qin, “Deep Neural Networks-Based Rolling Bearing Fault Diagnosis Using Fault Impulses,” Expert Syst. Appl., vol. 196, pp. 116-127, Jul. 2022.
[10] W. Zhang, G. Peng, C. Li, Y. Chen, and Z. Zhang, “A New Deep Learning Model for Fault Diagnosis With Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals,” Sensors, vol. 17, no. 2, p. 425, Feb. 2017. https://doi.org/10.3390/s17020425
[11] O. Sigmund and K. Maute, “Topology Optimization Approaches,” Struct. Multidiscip. Optim., vol. 48, no. 6, pp. 1031-1055, Dec. 2013. https://doi.org/10.1007/s00158-013-0978-6
[12] M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations,” J. Comput. Phys., vol. 378, pp. 686-707, Feb. 2019. https://doi.org/10.1016/j.jcp.2018.10.045
[13] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” arXiv preprint arXiv:1707.06347, Jul. 2017.
[14] Z. Liu, L. Liu, T. Yang, and W. Zhao, “Digital Twin-Driven Rapid Individualised Designing of Automated Flow-Shop Manufacturing System,” IEEE Trans. Ind. Inform., vol. 15, no. 4, pp. 2382-2394, Apr. 2019.
[15] Rout, P., Jha, A. K., Gupta, P., Singh, B. and Choudhury, S., “Failure analysis of composite plate under ballistic impact,” Mater. Today Proc., vol. 74, pp. 1008-1011, 2023. https://doi.org/10.1016/j.matpr.2022.11.357
[15] Ruhela, H. S., Bhardwaj, S., Agrawal, T. and Gupta, P., “Explicit dynamics analysis of shin pads using finite element analysis,” in Int. Conf. Industrial Problems on Machines and Mechanism, Springer, Singapore, 2022, pp. 683-690. https://doi.org/10.1007/978-981-99-4270-1_67
[17] Kumar, M., Rajiyan, J. and Gupta, P., “A computational approach for solving elasto-statics problems,” Mater. Today Proc., vol. 46, pp. 6876-6879, 2021. https://doi.org/10.1016/j.matpr.2021.04.462
