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

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.

Keywords

Additive manufacturing / Artificial intelligence-enabled design / Design automation / Artificial intelligence governance

Acknowledgments

ChatGPT 5.1/5.2 (OpenAI) and Grammarly were used for editorial work related to organization, readability, grammar, and presentation clarity. These tools were not used to generate technical content (including graphics), collect references or data, or drive the arguments or conclusions of this work. The authors have carefully reviewed and assume full responsibility for the manuscript.

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.

Author contributions

Conceptualization: Charul Chadha, Eman Hammad, Bhaskar Vajipeyajula, Albert E. Patterson

Methodology: Charul Chadha, Garth Crosby, Sabit Ekin, Mohamed Gharib, Eman Hammad, Congrui Jin, Ali Ahmad Malik, Calahan Mollan, Gaius C. Nzebuka, Vijitashwa Pandey, Donggil Song, Bhaskar Vajipeyajula, Albert E. Patterson

Project administration: Albert E. Patterson

Supervision: Albert E. Patterson

Visualization: Charul Chadha, Albert E. Patterson, Noemi Mendoza Diaz, Jisoo Park, Monsuru Ramoni

Writing - original draft: Charul Chadha, Albert E. Patterson, Congrui Jin, Ali Ahmad Malik, Gaius C. Nzebuka

Writing - review & editing: All authors

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

As this is a perspective article, no primary research results, data, software, or code have been included.

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