🤖 AI Summary
High computational cost of conventional additive manufacturing (AM) simulations impedes real-time process optimization for medium-scale, complex structures. This paper proposes a digital twin framework tailored for extrusion-based printing: it employs G-code-driven adaptive octree voxelization to represent part geometry, and—uniquely—leverages the same parallel adaptive octree mesh simultaneously for high-resolution geometric modeling and transient thermo-phase-change coupled simulation, enabling tight integration of geometry generation and physics solving. The method supports real-time prediction of thermal field evolution for complex topologies—including variable-density infill—while preserving high voxel resolution and significantly enhancing simulation scalability. Experimental results demonstrate a 10–100× speedup over traditional approaches. The framework provides a deployable virtual process exploration platform that facilitates print quality improvement, waste reduction, and intelligent parameter optimization.
📝 Abstract
Accurate simulation of the printing process is essential for improving print quality, reducing waste, and optimizing the printing parameters of extrusion-based additive manufacturing. Traditional additive manufacturing simulations are very compute-intensive and are not scalable to simulate even moderately-sized geometries. In this paper, we propose a general framework for creating a digital twin of the dynamic printing process by performing physics simulations with the intermediate print geometries. Our framework takes a general extrusion-based additive manufacturing G-code, generates an analysis-suitable voxelized geometry representation from the print schedule, and performs physics-based (transient thermal and phase change) simulations of the printing process. Our approach leverages parallel adaptive octree meshes for both voxelated geometry representation as well as for fast simulations to address real-time predictions. We demonstrate the effectiveness of our method by simulating the printing of complex geometries at high voxel resolutions with both sparse and dense infills. Our results show that this approach scales to high voxel resolutions and can predict the transient heat distribution as the print progresses. This work lays the computational and algorithmic foundations for building real-time digital twins and performing rapid virtual print sequence exploration to improve print quality and further reduce material waste.
