AccScience Publishing / IJAMD / Online First / DOI: 10.36922/IJAMD025510054
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PERSPECTIVE ARTICLE

Artificial intelligence and additive manufacturing as a coupled design system: Rethinking inference, manufacturability, and design education

Charul Chadha1 ,  Garth Crosby2 ,  Sabit Ekin2 ,  Mohamed Gharib2 ,  Eman Hammad2 ,  Congrui Jin2 ,  Ali Ahmad Malik3 ,  Noemi Mendoza Diaz2 ,  Calahan Mollan3 ,  Gaius C. Nzebuka2,4 ,  Vijitashwa Pandey3 ,  Jisoo Park2 ,  Monsuru Ramoni5 ,  Donggil Song2 ,  Bhaskar Vajipeyajula2 ,  Albert E. Patterson2,6*
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1 School of Mechanical and Materials Engineering, Washington State University, Pullman, Washington, United States of America
2 Department of Engineering Technology and Industrial Distribution, Texas A&M University, College Station, Texas, United States of America
3 Department of Industrial and Systems Engineering, Oakland University, Rochester, Michigan, United States of America
4 Department of Mechatronics Engineering, Federal University of Technology Owerri, Ihiagwa, Imo, Nigeria
5 Department of Manufacturing and Industrial Engineering, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
6 J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas, United States of America
IJAMD, 025510054 https://doi.org/10.36922/IJAMD025510054
Received: 18 December 2025 | Revised: 15 January 2026 | Accepted: 19 January 2026 | Published online: 6 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Artificial intelligence (AI) is becoming deeply integrated into additive manufacturing (AM) workflows, reshaping how designers approach geometry, materials, and process constraints. AI holds significant potential by accelerating design exploration, revealing complex patterns in AM behavior, and supporting earlier assessment of manufacturability. At the same time, it introduces new risks related to model transparency, data quality, physical validity, and the potential for overreliance by students and practitioners. This perspective examines these issues through four guiding questions that address the role of AI in AM-enabled design, the gaps that limit or enable AI contribution, the implications for engineering education, and the responsibilities of the research community in ensuring trustworthy and secure AI–AM integration. The main contributions of this perspective include: (i) Highlighting AI and AM as a coupled inference–fabrication system rather than independent tools; (ii) identifying zones of strong interdependence where inference and manufacturability interact; and (iii) articulating implications for design reasoning, education, and responsible research practice.

Graphical abstract
Keywords
Additive manufacturing
Artificial intelligence-enabled design
Design automation
Artificial intelligence governance
Funding
None.
Conflict of interest
Albert E. Patterson is one of the Guest Editors of this special issue, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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