Topological and material formation

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Abstract

Extrusion-based additive manufacturing (AM) is gaining traction in the construction industry, offering lower environmental and economic costs through reductions in material and production time. AM designs achieve these reductions by increasing topological and geometric complexity, and through variable material distribution via custom-programmed robot tool paths. Limited approaches are available to develop AM building designs within a topological^ free design search or to leverage material affects relative to structural performance. Established methods such as topological structural optimization (TSO) operate primarily within design rationalization, demonstrating less formal or aesthetic diversity than agent-based methods that exhibit behavioral character. While material-extrusion gravitational affects have been explored in AM research using viscous materials such as concrete and ceramics, established methods are not sufficiently integrated into simulation and structural analysis workflows. A novel three-part method is proposed for the design and simulation of extrusion-based AM that includes topoForm. an evolu-tionary multi-agent software capable of generating diverse topological designs: matForm. an agent-based AM robot tool-path generator that is geometrically agnostic and adapts material effects to local structural and geometric data: and matSim. a material-physics simulation environment that enables high-resolution AM material effects to be simulated and structurally and aesthetically analyzed. The research enables designers to incorporate and simulate material behavior prior to fabrication and produce instructions suitable for industrial robot AM. The approach is demonstrated in the generative design of four AM column-like elements.

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APA

Stuart-Smith, R., Danahy, P., & Rotta, N. R. L. (2020). Topological and material formation. In Proceedings of the 40th Annual Conference of the Association for Computer Aided Design in Architecture: Distributed Proximities, ACADIA 2020 (Vol. 1, pp. 290–299). ACADIA. https://doi.org/10.52842/conf.acadia.2020.1.290

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