Roadmap on artificial intelligence‐augmented additive manufacturing

Zolfagharian, A, Jin, L, Ge, Q, Liao, W-H, Díaz Lantada, A, Martínez, FF, Zhang, T, Liu, T, Wang, CCL, Mosallanejad, MH, Ghanavati, R, Saboori, A, de Blas de Miguel, A, Solórzano-Requejo, W, Cai, Y, Dong, X, Qu, H, Samadiani, N, Huang, G, Downey, A, Fu, Y, Yuan, L, Lee, T-K, Baniya, AA, Waseem, E, Sani, AR, Kouzani, AZ, Wu, Y, Nemitz, MP, Shirzad, M, Oh, D, Nam, SY, Bonatti, AF, Chiesa, I, Fortunato, GM, Vozzi, G, De Maria, C and Bodaghi, M ORCID: https://orcid.org/0000-0002-0707-944X, 2026. Roadmap on artificial intelligence‐augmented additive manufacturing. Advanced Intelligent Systems: e202500484. ISSN 2640-4567

Preview

Text 2560537_Bodaghi.pdf - Published version Download (15MB) | Preview

Abstract

Artificial intelligence-augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state-of-the-art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models to help solve problems in scalability, quality assurance, and material complexity, allowing for real-time defect detection, digital twin integration, and closed-loop process control. This roadmap brings together leading contributions from twenty internationally recognized research groups by uniting perspectives from materials science, computer science, robotics, and manufacturing. This work aims to create a cohesive framework for advancing AI2AM as a multidisciplinary science. The ultimate intent of this work is to establish a foundation for coordinated research and innovation in AI-powered AM and to serve as a strategic entry point for future breakthroughs in autonomous and sustainable production.

Item Type:	Journal article
Publication Title:	Advanced Intelligent Systems
Creators:	Zolfagharian, A., Jin, L., Ge, Q., Liao, W.-H., Díaz Lantada, A., Martínez, F.F., Zhang, T., Liu, T., Wang, C.C.L., Mosallanejad, M.H., Ghanavati, R., Saboori, A., de Blas de Miguel, A., Solórzano-Requejo, W., Cai, Y., Dong, X., Qu, H., Samadiani, N., Huang, G., Downey, A., Fu, Y., Yuan, L., Lee, T.-K., Baniya, A.A., Waseem, E., Sani, A.R., Kouzani, A.Z., Wu, Y., Nemitz, M.P., Shirzad, M., Oh, D., Nam, S.Y., Bonatti, A.F., Chiesa, I., Fortunato, G.M., Vozzi, G., De Maria, C. and Bodaghi, M.
Publisher:	Wiley
Date:	6 January 2026
ISSN:	2640-4567
Identifiers:	Number Type 10.1002/aisy.202500484 DOI 2560537 Other
Rights:	© 2026 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	23 Jan 2026 11:01
Last Modified:	23 Jan 2026 11:01
URI:	https://irep.ntu.ac.uk/id/eprint/55100

Actions (login required)

Edit View

Statistics

Views

Downloads