Researcher, Parametric Designer, Computational Designer, Industrial Designer, Manufacturing Engineer
COLLABORATORS
MJ Mayo, Ian Backstrom, Greg Reeseman
A study in translating biological complexity into manufacturable digital assets.
This project explores the generation of complex geometries through algorithmic growth models inspired by natural aggregation systems such as lichens, coral, and root structures.
Key Insights
Nature’s growth patterns became a framework for geometric optimization.
Additive manufacturing removes traditional complexity constraints, making it possible to manufacture geometries driven by natural growth logic rather than geometric convention. This project leveraged that opportunity to investigate structural and visual outcomes.
Additive manufacturing removes traditional complexity constraints, making it possible to manufacture geometries driven by natural growth logic rather than geometric convention. This project leveraged that opportunity to investigate structural and visual outcomes.
application
Generative systems guided by DFAM constraints produced print-ready organic geometries.Starting from a digital “seed,” I implemented algorithmic models that evolved shapes according to tunable growth parameters. These 2D simulations were extended into 3D volumes optimized for additive production using topology analysis and support minimization.
impact
A tangible expression of computational growth realized through additive means.
The resulting artifacts demonstrated a synergy between computational creativity and manufacturing capability—each piece a one-off product of defined parameters and emergent form.
The resulting artifacts demonstrated a synergy between computational creativity and manufacturing capability—each piece a one-off product of defined parameters and emergent form.
learnings + next steps
Refining simulation parameters will make computational growth both expressive and reliable.
A deeper understanding of generative models will improve the predictability of geometry and mechanical properties.
Refining simulation parameters will make computational growth both expressive and reliable.
A deeper understanding of generative models will improve the predictability of geometry and mechanical properties.