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3D solid model generation method based on a generative adversarial network
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AI
DOI：10.1007/s10489-022-04381-8.png)
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摘要
En 中文
Three-dimensional (3D) solid model generation technology is the foundation to realize intelligently generated structural design, but this problem has not yet been effectively solved. This paper proposes a comprehensive generation method named 3D-JointGAN for 3D solid models by combining a 3D generative adversarial network (GAN) and reverse engineering (RE) technology. First, the basic idea, relevant theories and specific implementation process of 3D-JointGAN are introduced. Then, the approach is applied to the generation of a three-branch cast-steel joint in practical engineering, and the mechanical properties of representative joints selected after evaluation are synthetically calculated. Finally, reduced-scale models of the representative joints are manufactured using 3D printing technology to verify the manufacturability of the generated models. By comparison with three other types of joints common in engineering, the results show that the proposed method has outstanding generation and optimization abilities and can generate a variety of innovative and highly vivid 3D solid models. Furthermore, the representative joints chosen after assessment have better mechanical properties. The method proposed in this paper solves the bottleneck problem of intelligently generated structural design and has broad application prospects.
Keyword:
Intelligent generation
Solid models
3D generative adversarial network
Reverse engineering
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
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