3d Printed Biodigital Fractal Bioreceptive Topologies From Diffusion Models and 2d Cnns Generated Design Through 2.5d Depth Mapping

AI-Aided Design (AIAD) tools have revolutionized the design process by rapid customized high-resolution renders. Yet, they were limited in their 3D-translation and direct fabrication. Currently, AI-models for 3D-depth-mapping are evolving and they require a criteria for their integration in the design process to maintain the human authorship and creativity to achieve sustainability. The current work reports an (AIAD) to fabrication study of Bio-receptive tiles for integration in the built environment. The experimental design methodology includes (AIAD) phases of prompt synthesis, data generation and augmentation. Employing AI-Diffusion models, Convolution Neural Networks, Recurrent Neural Networks, and transformer models. Followed by 2D to 3D-depth-mapping from a single 2D-image and 3D-printing into bio-receptive tiles. The 2D-CNN image-to-sequential data generation proved to be an efficient generative design tool with more control over image-generation operative parameters than Diffusion models. The 3D-printed bio-receptive tiles are time-material-cost sustainable with high resolution and multi-scale topologies for hosting microbial strains.