This paper introduces a material-informed woodcraft framework built upon knowledge-based creative design (KBCD) models, addressing the challenge of incorporating material-driven data, computational design principles, and fabrication constraints. The method of the paper comprises two stages: pre-processing and exploratory data analysis, data to design knowledge to part design transition. The significance of the framework lies in its ability to generate morphologies leveraging microstructural material data and its fabrication knowledge in a feature-based manner. The paper employs the Vision Transformer (ViT) machine learning (ML) algorithm, alongside computer numerical control (CNC) milling tools, to present a fabricated example that enables the generation of morphologies based on timber data and fabrication constraints. The framework emphasises the significance of design knowledge arising from material data, marking a critical juncture where designers can intervene and guide the design process as they limit their search space by introducing constraints. This work offers a roadmap for integrating microstructural 3D solid models as a new data type in design and craft workflows.
Seda Zirek (Mon,) studied this question.