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

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

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