ArticleScientific reports2024
Accelerating the design of lattice structures using machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.Materials (Basel, Switzerland) · 2026Review
- From Structural Kinematics to Thermomechanical Degradation in Polymer and Hybrid Negative Thermal Expansion Metamaterials.Polymers · 2026Review
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Metallic topological structures in bone repair implants: Design, properties, and biological interactions.Journal of orthopaedic translation · 2026Review
- Biomimetic Lattice Structures Design and Manufacturing for High Stress, Deformation, and Energy Absorption Performance.Biomimetics (Basel, Switzerland) · 2025Review
- Recent Advances in 3D Printed Electrodes - Bridging the Nano to Mesoscale.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Prediction of Mechanical Properties of Lattice Structures: An Application of Artificial Neural Networks Algorithms.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R
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Registered trials
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