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This paper examines how the evolution of architectural generative design processes aim to apply similar physical and geometrical principles of biological processes taking place during development and to translate them to fabrication processes. In analogy to the reaction-diffusion mechanism for biological pattern prediction, the logic of stripe is used as construction system and examined for its structural behaviour. Both, mesh relaxation processes and weighted mesh graphs representations are employed as design tools for the construction of a minimal thin shell structural skin with branching topologies. Eventually the design workflow is extended to engage also collaborative fabrication processes and to steer the design based on intuition, knowledge of the fabrication tools, properties of the materials, manufacturing simulations and logic of assemble. This approach could lead to the optimization of material usage and machine time and facilitate the assembly process of a physical object which integrates the whole process into its form. The outcomes have been used to fabricate a prototype, using three different materials and digital fabrication methods, to examine the stability and the mechanical connectivity by taking in count the tolerances. The paper argues that biological skin patterns and segmentation in fabrication open a new field of interdisciplinary investigation and architectural applications.

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This article explains the evolution towards the subject of digital fabrication of thin shell structures, searching for the computational design techniques which allow to implement biological pattern mechanisms for efficient fabrication procedures. The method produces data sets in order to analyse and evaluate parallel alternatives of branching topologies, segmentation patterns, material usage, weight and deflection values as a user learning process. The importance here is given to the selection of the appropriate attributes, referring to which specific geometric characteristics of the parametric model are affecting each other and with what impact. The outcomes are utilized to train an Artificial Neural Network to predict new building information based on new combinations of desired parameters so that the user can decide and adjust the design based on the new information.