| id |
architectural_intelligence2024_25 |
| authors |
Ana Goidea, Mariana Popescu, Anton Tetov Johansson & David Andréen |
| year |
2024 |
| title |
Algorithmic modeling of functionally graded metamaterials in 3D printed building envelopes |
| doi |
https://doi.org/https://doi.org/10.1007/s44223-024-00068-x
|
| source |
Architectural Intelligence Journal |
| summary |
Recent development of powder-bed additive manufacturing promises to enable the production of architectural structures that combine high resolution and articulation with economies of scale. These capabilities can potentially be used for functionally graded metamaterials as part of the building envelope and structure, paving the way for new functionalities and performances. However, designing such multifunctional structures requires new design and modelling strategies to control, understand, and generate complex geometries and their transcalar interdependencies. The work presented here demonstrates a modeling framework that can unite multiple generative and organizational algorithms to create a unified, 3D printable building element that integrates a range of functional requirements. Our methods are based on an understanding of stigmergic principles for self-organization and developed to allow for a wide range of application scenarios and design intents. The framework is structured around a composite modeling environment based on a combination of volumetric modeling and particle-spring systems, and is developed to negotiate the large scalar range necessary for such applications. We present here a prototype demonstrator designed using this framework: Meristem Wall, a functionally integrated building envelope fabricated through a combination of powder bed 3D printing and CNC knitting. |
| series |
Architectural Intelligence |
| email |
|
| full text |
 file.pdf (7,516,390 bytes) |
| references |
Content-type: text/plain
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| last changed |
2025/01/09 15:05 |
