Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing
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.
Authors
- Amedeo Franco Bonatti (ORCID: https://orcid.org/0000-0001-7177-5135)
- Reza Ghanavati (ORCID: https://orcid.org/0000-0002-8562-0422)
- Yanzhou Fu (ORCID: https://orcid.org/0000-0002-5471-9972)
- Wei‐Hsin Liao
- Qi Ge (ORCID: https://orcid.org/0000-0002-1493-6120)
- Gabriele Maria Fortunato (ORCID: https://orcid.org/0000-0002-0260-4014)
- Austin Downey (ORCID: https://orcid.org/0000-0002-5524-2416)
- Yi Zhong Cai
- Arbind Agrahari Baniya (ORCID: https://orcid.org/0000-0002-9359-6506)
- Lang Yuan
- Najmeh Samadiani (ORCID: https://orcid.org/0000-0002-0108-3456)
- Huangyi Qu (ORCID: https://orcid.org/0009-0006-6478-6710)
- Masoud Shirzad
- Eisha Waseem
- Ali Zolfagharian (ORCID: https://orcid.org/0000-0001-5302-360X)
- Liuchao Jin (ORCID: https://orcid.org/0000-0001-6204-1922)
- Markus P. Nemitz (ORCID: https://orcid.org/0000-0003-0771-5407)
- Yijia Wu (ORCID: https://orcid.org/0000-0003-4379-9863)
- Abdul Rahman Sani
- Tianyu Zhang (ORCID: https://orcid.org/0000-0002-8990-8212)
- Dageon Oh (ORCID: https://orcid.org/0009-0006-2687-0890)
- F. Fernández Martínez
- Abbas Kouzani
- Andrés Díaz Lantada
- Tao Liu
- William Solórzano‐Requejo
- Xiangyang Dong
- Charlie C. L. Wang
- Seung Yun Nam
- Mahdi Bodaghi
- Abdollah Saboori
- Guangyan Huang
- Carmelo De Maria
- Giovanni Vozzi
- Mohammad Hossein Mosallanejad
- Tsz‐Kwan (Glory) Lee
- Alejandro De Blas De Miguel
- Irene Chiesa
Institutions
- University of Pisa (IT)
- Tufts University (US)
- Piaggio (Italy) (IT)
- Guangdong University of Technology (CN)
- Deakin University (AU)
- University of South Carolina (US)
- Politecnico di Torino (IT)
- Chinese University of Hong Kong (HK)
- University of Manchester (GB)
- Southern University of Science and Technology (CN)
- IMDEA Materials (ES)
- Chinese University of Hong Kong, Shenzhen (CN)
- CSIRO Manufacturing (AU)
- Nottingham Trent University (GB)
- Universidad Politécnica de Madrid (ES)
- University of Hong Kong (HK)
- Pukyong National University (KR)
Publication Details
- Journal
- Advanced Intelligent Systems
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1002/aisy.202500484
- Citations
- 1
- Primary Topic
- Additive Manufacturing and 3D Printing Technologies
- Type
- article
- Field-Weighted Citation Impact
- 23.73