GRAFT
a relational data framework for robotic tim-ber fabrication and assembly
Abstract. This paper presents a
graph-based data structure for robotic timber fabrication that unifies
reciprocal structural information, assembly sequencing, and inverted robotic
path planning within a shared relational framework. Current fabrication
workflows in the Architecture, Engineering, and Construction (AEC) industry are
dominated by linear and opaque data exchanges, limited interoperability within
current industry, and limited adaptability to new designs. By contrast, GRAFT (Graph-Based
Robotic Assembly and Fabrication for Timber) formalizes
fabrication dependencies as a graph network, linking data nodes and edges to material
stock and generating fabrication data through explicit edge relationships. This
structure allows design intent, material properties, and fabrication logic to
co-evolve dynamically throughout robotic production. A prototype implementation
demonstrates how fabrication data derived from the graph can be decomposed into
modular machining operations and executed through a multi-tool robotic setup.
The approach integrates standard industrial data protocols while maintaining compatibility
with emerging BIM-graph data frameworks. Results from a reciprocal timber frame
case study highlight the framework’s capacity to improve data transparency,
enable adaptive assembly sequencing, and optimize robotic timber fabrication.
The proposed method contributes to developing scalable, machine-driven data
infrastructures that connect design, material, and fabrication intelligence
promoting adaptive, automation-ready timber production. Theme D: Robots +
Emerging Methodologies.
Keywords: Robotic fabrication, Graph-based data models, Digital timber construction, Relational data structures, BIM integration.
Keywords: Robotic fabrication, Graph-based data models, Digital timber construction, Relational data structures, BIM integration.