AccScience Publishing / ESAM / Online First / DOI: 10.36922/ESAM025110006
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REVIEW ARTICLE

Generative artificial intelligence in lattice structure design for additive manufacturing: A critical review

Jinlong Su1* ,  Yang Mo1 ,  Swee Leong Sing1*
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1 Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore, Singapore
ESAM 2025, 1(1), 025110006 https://doi.org/10.36922/ESAM025110006
Received: 8 February 2025 | Revised: 17 March 2025 | Accepted: 18 March 2025 | Published online: 21 March 2025
© 2025 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

Lattice structures, characterized by their lightweight yet high-strength properties, energy absorption capabilities, and superior thermal management, have become integral in advanced additive manufacturing (AM) applications. However, designing optimized lattice structures that balance mechanical performance, manufacturability, and functional requirements remains a complex and computationally intensive challenge. Recently, generative artificial intelligence (Gen-AI) has emerged as a transformative approach, offering automated and efficient solutions for lattice structure design. This review explores the application of Gen-AI in lattice structure design and optimization for AM. Gen-AI enables automated inverse design, generating lattice structures that meet predefined functional and mechanical targets, reducing trial-and-error efforts. It supports performance optimization by enhancing mechanical strength, energy absorption, and thermal efficiency when minimizing material usage and weight. Besides, Gen-AI also facilitates process-aware design by integrating AM-oriented constraints, such as build orientation, support strategies, and residual stress, to improve manufacturability and reduce post-processing. In addition, it accelerates simulations by expediting performance prediction and reducing computational costs. Despite the growing importance of Gen-AI in AM lattice structure, comprehensive reviews on this topic remain limited. This work addresses this gap, providing critical insights into current advancements, key challenges, and future perspectives, aiming to guide the integration of Gen-AI into advanced lattice structure design for AM and support the development of next-generation high-performance structures.

Keywords
Generative artificial intelligence
Lattice structure
Additive manufacturing
Deep learning
Design and optimization
Funding
This work is fully supported by the Advanced Research and Technology Innovation Centre, the National University of Singapore under Grant (project number: ADT-RP1). Yang Mo acknowledges the sponsorship of the China Scholarship Council (No. 202306130143).
Conflict of interest
Dr. Swee Leong Sing is the Editor-in-Chief of this journal, 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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