Lattice Object Modeling for Faster Additive Manufacturing Simulation

The lattice structure is divided into multiple unit cells, each processed independently through the generative adversarial network. This segmentation allows parallel processing of individual cells, reducing overall computational time while maintaining high resolution in each cell. The GAN learns from a dataset of unit cells and generates new unit cells that can be assembled into the complete lattice structure, avoiding the need to process the entire high-resolution structure at once.

The generative adversarial network is pre-trained on a dataset of unit cells before actual lattice generation. This preliminary training phase allows the GAN to learn the statistical properties and structural patterns of lattice unit cells, enabling it to rapidly generate high-resolution unit cells during the actual design process without requiring repeated high-cost simulations for each cell generation.

The complete lattice structure is segmented into repeating unit cells that are processed independently. The GAN generates and optimizes individual unit cells, which are then assembled to form the complete lattice structure. This segmentation reduces computational resource consumption by focusing simulations on representative unit cells rather than the entire high-resolution structure, while maintaining simulation fidelity through accurate unit cell generation.

Instead of simulating every high-resolution element of the complete lattice structure, the system uses the GAN to generate representative unit cells that capture the essential structural and mechanical properties. These copied and repeated unit cells approximate the behavior of the complete high-resolution structure, providing sufficient simulation fidelity at reduced computational cost.

The lattice structure is segmented into standardized unit cells with characteristic dimensions suitable for additive manufacturing. The GAN processes and optimizes these unit cells at a manageable scale, generating detailed microscopic structures within each cell while maintaining processing speeds compatible with modern CAD systems. The segmented approach allows the system to achieve detailed structural performance without being overwhelmed by the computational complexity of processing the entire microscopic lattice at once.

The system uses the GAN to learn and generate unit cells with optimized geometric parameters that achieve desired structural performance. By changing and optimizing parameters such as strut thickness, cell geometry, and material distribution within unit cells, the system achieves high structural performance at the microscopic level while maintaining computational efficiency through parameter-based generation rather than full high-resolution modeling.