Machine learning based topology optimization of fiber orientation for variable stiffness composite structures

DOI10.1002/NME.6809zbMATH Open1548.74617MaRDI QIDQ6554029

Yunkai Gao, Yanan Xu, Guangyong Sun, Jianguang Fang, Chi Wu, G. P. Steven, Qing Li

Publication date: 12 June 2024

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

zbMATH Keywords

artificial neural networkstopology optimizationmachine learningfiber orientationactive learning

Mathematics Subject Classification ID

Topological methods for optimization problems in solid mechanics (74P15)

Cites Work

• A critical review of established methods of structural topology optimization
• On the usefulness of non-gradient approaches in topology optimization
• First-order sequential convex programming using approximate diagonal QP subproblems
• Discrete material optimization of vibrating laminated composite plates for minimum sound radiation
• A level set method for structural topology optimization.
• Multiscale topology optimization using neural network surrogate models
• Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization
• Artificial neural networks in structural dynamics: a new modular radial basis function approach vs. convolutional and feedforward topologies
• A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
• Topology optimization for concurrent design of structures with multi-patch microstructures by level sets
• A bi-value coding parameterization scheme for the discrete optimal orientation design of the composite laminate
• Discrete material optimization of general composite shell structures
• The method of moving asymptotes—a new method for structural optimization
• Level-set topology optimization for maximizing fracture resistance of brittle materials using phase-field fracture model
• Level set topology optimization for design-dependent pressure load problems

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