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◆ Advanced Science2026-02-15· Scalability

Lattice Genome Framework for Regionally Tailored Component‐Level Multi‐Objective Design in Additive Manufacturing

Haoyuan Deng, Yufan Zhao, Mingyang Cao, Haiou Yang, Pan Wang, Hongze Wang, Hao Wang, Akihiko Chiba, Xin Lin

原始摘要(英文原文)· Original abstract
Lattice structures fabricated by additive manufacturing provide unprecedented opportunities for lightweight design, mechanical regulation, and functional integration. However, their efficient development remains constrained by vast design spaces and complex structure-property relationships. Inspired by the Materials Genome Initiative, we propose a Lattice Genome framework, a data-driven approach that integrates high-throughput simulations and performance databases to systematically map lattice properties. Specifically, we develop a component-level, regionally programmable, multi-objective design strategy for coordinated structure-property regulation. The efficacy of the proposed framework is demonstrated through two representative cases: (1) tailoring internal stress distributions to match preset targets under constant relative density, and (2) enhancing load capacity (62% higher than the conventional design) while redirecting failure away from critical zones in cavity-containing components. Overall, this work establishes a generalizable and scalable paradigm for intelligent lattice design and provides a transferable, data-centric platform that lays the groundwork for efficient, multifunctional applications in additive manufacturing.
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Lattice Genome Framework for Regionally Tailored Component‐Level Multi‐Objective Design in Additive Manufacturing — 科研速览 Science Skim