Neuronal Stool

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This research develops a shape design engine that allows users to explore design possibilities for a stool to be cast using 3D-printed mould and aluminium. The engine mediates between design freedom and constraints (castability, stability, and cost) for users to reach their favorite designs with desired properties.

The challenges are:

1.generate design variations that are fabricatable
2.create a user interface that can navigate users through the catalogues of the design alternatives and their properties

The result demonstrates the potential of human-machine collaboration in exploring design space.

Overview
Period: 2018.06 - 2018.09
Location: Zurich, Switzerland
Type: Thesis

Collaboration
Advisor: Mania Aghaei Meibodi and Benjamin Dillenburger
Team: ZongRu Wu
Role: Design, Fabtication, Documentation, Presentation

Technology
Software/Language: Unity, C#, Houdini, Python, Netfab
Machine/Fabrication Method: ExOne, Aluminium Casting
Keywords:agent-based modeling, machine learning, generative design, material computation