@Article{cmc.2026.087308,
AUTHOR = {Hao Sun, Xiaohong Ding, Min Xiong, Heng Zhang},
TITLE = {Data-Driven Conditional Diffusion Generation Method for Anisotropic Mechanical Metamaterial Unit Cells},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {89},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v89n2/68836},
ISSN = {1546-2226},
ABSTRACT = {Designing two-dimensional anisotropic mechanical metamaterial unit cells from prescribed effective properties remains a challenging inverse problem, particularly when directional stiffness and material usage need to be controlled simultaneously. In this work, a data-driven conditional diffusion framework is developed for generating unit-cell structures with target effective elastic moduli and volume fractions. A structure–property database containing 57,000 binary unit-cell images is first established through a random target-property-driven inverse homogenization method. The effective elastic moduli in the <i>x</i> and <i>y</i> directions, together with the volume fraction, are used as conditional labels, denoted as (<i>E</i><sub><i>x</i></sub>, <i>E</i><sub><i>y</i></sub>, <i>V</i>). A conditional denoising diffusion probabilistic model (DDPM) with a U-Net denoising backbone, referred to as DDPM-UNet, is then trained to generate unit-cell layouts under prescribed property conditions. To improve the consistency between generated geometries and target properties, a convolutional neural network (CNN) surrogate model is introduced. The CNN provides a property-consistency loss during diffusion model fine-tuning and is also used as a fast evaluator for candidate screening during inference. Compared with DDPM-UNet, the DDPM-UNet-CNN framework produces more valid candidates under 5%, 10%, and 20% relative error thresholds. FEA-based re-homogenization further shows that the selected candidates generally approach the prescribed effective properties. The developed framework provides a practical generative route for identifying candidate anisotropic mechanical metamaterial unit cells under prescribed property conditions.},
DOI = {10.32604/cmc.2026.087308}
}