Generative AI-Assisted Engineering Design Optimization for Sustainable Manufacturing

  • Authors

    • Shalini Gupta Associate Professor, Panjab University, India. Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2025PII6A9W

    Published 07-04-2025

  • Generative Artificial Intelligence, Engineering Design Optimization, Sustainable Manufacturing, Multi-objective Optimization, Intelligent Design Automation, Digital Engineering

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    Generative AI-Assisted Engineering Design Optimization for Sustainable Manufacturing. (2025). International Journal of Modern Research in Science & Engineering, 8(2), 01-19. https://doi.org/10.67228/30716357/IJMRSE-2025PII6A9W

  • Abstract

    Sustainable manufacturing increasingly relies on Generative AI to optimize engineering design while reducing environmental impact, resource consumption, and production costs. By integrating deep learning, large language models, diffusion models, reinforcement learning, and digital engineering, Generative AI enables autonomous design generation, multi-objective optimization, and lifecycle sustainability assessment. The proposed framework enhances material efficiency, energy savings, carbon reduction, and manufacturing flexibility while accelerating product development. Despite challenges such as explainability, computational complexity, and design validation, Generative AI offers significant potential to transform sustainable manufacturing through intelligent, environmentally responsible, and data-driven engineering design.

  • References

    [1] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, updated perspectives for engineering AI, 2022.

    [2] A. Vaswani et al., “Transformers for machine intelligence: Recent advances in engineering applications,” IEEE Access, vol. 11, pp. 115243–115269, 2023.

    [3] J. Jumper et al., “Artificial intelligence and generative models for scientific and engineering design,” Nature Reviews Methods Primers, vol. 4, no. 1, pp. 1–22, 2024.

    [4] S. Bubeck et al., “Sparks of artificial general intelligence: Early experiments with GPT-4,” Commun. ACM, vol. 67, no. 2, pp. 52–65, 2024.

    [5] X. Liu, H. Wang, Y. Zhang, and J. Chen, “Generative artificial intelligence for intelligent engineering design: A comprehensive review,” IEEE Access, vol. 12, pp. 45231–45259, 2024.

    [6] Z. Ren, M. Li, and Y. Guo, “Generative AI-enabled computer-aided design and manufacturing: Recent advances and future directions,” IEEE Transactions on Industrial Informatics, vol. 20, no. 5, pp. 6248–6262, 2024.

    [7] M. R. Khosravi, P. Angelov, and S. Nahavandi, “Explainable artificial intelligence for engineering optimization and intelligent manufacturing,” IEEE Transactions on Industrial Informatics, vol. 20, no. 3, pp. 3148–3161, 2024.

    [8] J. Lee, H. Davari, J. Singh, and V. Pandhare, “Industrial AI and digital twins for smart manufacturing,” IEEE Transactions on Industrial Informatics, vol. 19, no. 8, pp. 8972–8985, 2023.

    [9] S. K. Gupta, A. Bernard, and D. Mourtzis, “Digital twin-driven intelligent product design and lifecycle engineering: A review,” Journal of Manufacturing Systems, vol. 71, pp. 275–296, 2024.

    [10] Y. Lu, X. Xu, and L. Wang, “Sustainable smart manufacturing enabled by artificial intelligence and digital twins,” Robotics and Computer-Integrated Manufacturing, vol. 84, Art. no. 102596, 2023.

    [11] D. Mourtzis, N. Angelopoulos, and E. Panopoulos, “Artificial intelligence in sustainable manufacturing systems: State of the art and future trends,” Procedia CIRP, vol. 119, pp. 58–65, 2023.

    [12] H. Zhang, Y. Chen, and L. Li, “Multi-objective optimization for sustainable manufacturing using evolutionary algorithms and machine learning,” IEEE Access, vol. 12, pp. 68211–68230, 2024.

    [13] J. Cao, X. Zhou, and Y. Sun, “Machine learning-assisted engineering design optimization: Techniques and industrial applications,” Engineering Applications of Artificial Intelligence, vol. 129, Art. no. 107621, 2024.

    [14] Y. Wang, Z. Huang, and X. Xu, “Generative AI for topology optimization and lightweight engineering design,” Computer-Aided Design, vol. 173, Art. no. 103742, 2025.

    [15] X. Chen, L. Wang, Y. Liu, and J. Li, “Generative artificial intelligence for sustainable product design and smart manufacturing: Challenges and opportunities,” IEEE Access, vol. 13, pp. 18325–18349, 2025.

    [16] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.

    [17] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845.

    [18] Seknametla, P. R., & Sunkara, R. (2023). Platform engineering and internal developer platforms: Measuring cognitive load reduction and developer productivity in self-service infrastructure models. International Journal of Computer Techniques, 10(4).

    [19] Seknametla, P. R., Abduhur, R., Siddhanti, P., Thangam, V. T., & Giridhar Kumar, M. (2025). Comprehensive analysis for health monitoring using wearable sensor networks. In 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) (pp. 1–6). Bengaluru, India. IEEE. https://doi.org/10.1109/ICICKE65317.2025.11136251

  • Downloads