To translate the point cloud resulting from the machine learning process into a volumetric model, a set of project-specific bio-computational design tools and optimisation strategies is developed, allowing for the design and fabrication of high-resolution living architectures. Proximity algorithm is used as a method to evaluate the density of the pointcloud which would optimise it for the fabrication process, however would still keep the complexity of the biological matter. The discontinuous volumetric data sets translate the material organisation principles of wood through the gradients of digital fiber densities. This network of lines and points describes the distribution of material as a behavioural pattern, containing information on the material organisation such as: allocation of stiff and soft materials within a structure and gradients of fibre densities, as well as variation in hydrophilic properties.
The material pillow is designed to grow vertically, evoking the idea of a tree—a traditional archetype of an architectural column. The morphological exploration follows the logic of material distribution, learning the complexity of ecological system. Revealing the memory of each section cut of a tree, the material becomes an augmented library of knowledge from different tree species represented in one designed piece.
In order to relate the point to the synthetic material system each point is equalized to a voxel with specific volume and weight. Every digital voxel is translated into the voxel of physical matter - synthetic wooden material. Each voxel of this synthetic object could be equalised with the particle of wood which comes from the living tree and keeps its ability to store the carbon. Measuring the full biomass of the voxelised point cloud, we could estimate its carbon capturing potential of the overall structure.