Topological and Material Formation: A Generative Design Framework for Additive Manufacturing Integrating Material-Physics Simulation and Structural Analysis

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 topologically 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 evolutionary 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.

Stuart-Smith, R, Danahy, P, Revelo, N. “Topological and Material Formation: A Generative Design Framework for Additive Manufacturing Integrating Material-Physics Simulation and Structural Analysis.” ACADIA 2020: Distributed Proximities, Acadia. 2020. 290–299

The paper is accessible on the cumincad website here.

The Autonomous Manufacturing Lab (AML) explores the integration of robotic manufacturing and construction within architectural design. The AML aims to reduce the environmental and economic costs of design and production while enhancing their cultural and aesthetic impact. The lab's integrative methods for generative design and manufacturing are developed as semi-autonomous systems, exploring possibilities for artisanal-like craftsmanship to be scaled to large production volumes. This approach provides greater specificity and enhanced aesthetic character in design responses compared to traditional automated methods, whilst also reducing material use and waste within bespoke design and production processes. The AML's research enhances design and production capabilities across diverse manufacturing methods and materials by developing industrial and mobile robot fabrication techniques, multi-agent systems, AI/machine learning computational approaches, and applications of real-time sensor and computer vision technologies. This multidisciplinary research includes the development of Aerial Additive Manufacturing, recently published in Nature, which demonstrates the world’s first in-flight additive manufacturing by cooperating drones, representing a rapidly deployable form of collective robotic construction.

University of Pennsylvania Weitzman School of Design
The Weitzman School of Design prepares students to address complex sociocultural and environmental issues through thoughtful inquiry, creative expression, and innovation. As a diverse community of scholars and practitioners, we are committed to advancing the public good–both locally and globally–through art, design, planning, and preservation.

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Topological and Material Formation: A Generative Design Framework for Additive Manufacturing Integrating Material-Physics Simulation and Structural Analysis

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