4.6 Article

Generative machine learning algorithm for lattice structures with superior mechanical properties

期刊

MATERIALS HORIZONS
卷 9, 期 3, 页码 -

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/d1mh01792f

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资金

  1. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2020R1A6A3A03039104]
  2. Hellman Fellows Program
  3. Office of Naval Research [N00014-21-1-2604]
  4. National Research Foundation of Korea [2020R1A6A3A03039104] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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The design of lattice structures involves a trade-off between density and mechanical properties. This study introduces a deep learning approach using high-order Bezier curves to explore the optimized shape of beam elements in lattice structures. The optimized design smartly distributes material towards the weakest location, achieving the highest modulus and strength. Experimental validation shows that the optimized design outperforms benchmark ones in terms of modulus and strength.
Lattice structures are typically made up of a crisscross pattern of beam elements, allowing engineers to distribute material in a more structurally effective way. However, a main challenge in the design of lattice structures is a trade-off between the density and mechanical properties. Current studies have often assumed the cross-sectional area of the beam elements to be uniform for reducing the design complexity. This simplified approach limits the possibility of finding superior designs with optimized weight-to-performance ratios. Here, the optimized shape of the beam elements is investigated using a deep learning approach with high-order Bezier curves to explore the augmented design space. This is then combined with a hybrid neural network and genetic optimization (NN-GO) adaptive method for the generation of superior lattice structures. In our optimized design, the distribution of material is smartly shifted more towards the joint region, the weakest location of lattice structures, to achieve the highest modulus and strength. This design strikes to balance between two modes of deformation: axial and bending. Thus, the optimized design is efficient for load bearing and energy absorption. To validate our simulations, the optimized design is then fabricated by additive manufacturing and its mechanical properties are evaluated through compression testing. A good correlation between experiments and simulations is observed and the optimized design has outperformed benchmark ones in terms of modulus and strength. We show that the extra design flexibility from high-order Bezier curves allows for a smoother transition between the beam elements which reduces the overall stress concentration profile.

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