Rapid solving model for lattice mechanical properties based on CNN-GRU algorithm
Source abstract
Abstract
Abstract To enhance the efficiency of solving mechanical properties of lattice materials, this paper constructs a rapid-solving model based on the CNN-GRU algorithm. Initially, methods for constructing the mathematical model of lattice units are investigated, and the equivalent Young’s modulus, shear modulus, and Poisson’s ratio are derived using the asymptotic homogenization theory. Subsequently, a voxel model based on the lattice structure is established, and a hybrid CNN-GRU model is developed to achieve rapid solving of key mechanical properties. Finally, the accuracy and efficiency of the model are verified. The results indicate that the CNN-GRU model significantly surpasses traditional finite element methods in computational efficiency, achieving a solution accuracy exceeding 92 %, thereby demonstrating the model’s effectiveness and adaptability. This research provides a novel approach for the design and optimization of lattice materials and contributes to the advancement of additive manufacturing technologies in material applications.
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Research context
OpenAlex topics: Applied Advanced Technologies · Machine Learning in Materials Science · Advanced Technologies and Applied Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics and Information Engineering, Tiangong University , Tianjin 300387 , China
Key Laboratory of Digital Twin Generic Technology in Special Equipment for State Market Regulation, Tianjin Special Equipment Inspection Institute , Tianjin 300192 , China
School of Mechanical Engineering, Tiangong University , Tianjin 300387 , China
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