Three-dimensional Deep Shape Optimization with a Limited Dataset
This work addresses shape optimization for mechanical design under data constraints, but it appears incremental as it builds on existing methods with specific enhancements.
The study tackled the problem of applying generative models to mechanical design with limited datasets by proposing a deep learning-based optimization framework, which demonstrated robustness and effectiveness in multi-objective shape optimization on 3D datasets like wheels and cars.
Generative models have attracted considerable attention for their ability to produce novel shapes. However, their application in mechanical design remains constrained due to the limited size and variability of available datasets. This study proposes a deep learning-based optimization framework specifically tailored for shape optimization with limited datasets, leveraging positional encoding and a Lipschitz regularization term to robustly learn geometric characteristics and maintain a meaningful latent space. Through extensive experiments, the proposed approach demonstrates robustness, generalizability and effectiveness in addressing typical limitations of conventional optimization frameworks. The validity of the methodology is confirmed through multi-objective shape optimization experiments conducted on diverse three-dimensional datasets, including wheels and cars, highlighting the model's versatility in producing practical and high-quality design outcomes even under data-constrained conditions.