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Roadmap on Artificial Intelligence-Augmented Additive Manufacturing

delete2026-01-01
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OA
AI
C
Chen, Minglong *
L
Liuchao Jin
Q
Qi Ge
W
Wei‐Hsin Liao *
A
Andrés Díaz Lantada *
F
F. Fernández Martínez
T
Tianyu Zhang
T
Tao Liu
C
Charlie C. L. Wang *
M
Mohammad Hossein Mosallanejad
R
Reza Ghanavati
A
Abdollah Saboori *
A
Alejandro De Blas De Miguel
W
William Solórzano‐Requejo *
Y
Yi Zhong Cai *
X
Xiangyang Dong
H
Huangyi Qu
N
Najmeh Samadiani *
G
Guangyan Huang
A
Austin Downey *
Y
Yanzhou Fu
L
Lang Yuan
T
Tsz‐Kwan (Glory) Lee *
A
Arbind Agrahari Baniya
E
Eisha Waseem
A
Abdul Rahman Sani
A
Abbas Kouzani
Y
Yijia Wu
M
Markus P. Nemitz *
M
Masoud Shirzad
D
Dageon Oh
S
Seung Yun Nam *
A
Amedeo Franco Bonatti
I
Irene Chiesa
G
Gabriele Maria Fortunato
G
Giovanni Vozzi
C
Carmelo De Maria *
M
Mahdi Bodaghi *
DOI:10.1002/aisy.202500484delete
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Abstract

Abstract

En 中文
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.
Keywords:
3D printing
additive manufacturing
Ai2AM
artificial intelligence
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Journal

Advanced Intelligent Systems cover
Advanced Intelligent Systems
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6.1
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