ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing

• URL: http://arxiv.org/abs/2510.02009v1
• Date: Thu, 02 Oct 2025 13:30:20 GMT
• Title: ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
• Authors: Giacomo Rizzieri, Federico Lanteri, Liberato Ferrara, Massimiliano Cremonesi,
• Abstract summary: This work introduces ShapeGen3DCP, a deep learning framework for prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP)<n>The framework is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes.<n>The training dataset was synthetically generated using a well-established Particle Finite Element (PFEM) model of 3DCP, overcoming the scarcity of experimental data.
• Score: 0.4666493857924357
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work introduces ShapeGen3DCP, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are reformulated into dimensionless parameters that capture underlying physical principles. Predicted geometries are compactly represented using Fourier descriptors, which enforce smooth, closed, and symmetric profiles while reducing the prediction task to a small set of coefficients. The training dataset was synthetically generated using a well-established Particle Finite Element (PFEM) model of 3DCP, overcoming the scarcity of experimental data. Validation against diverse numerical and experimental cases shows strong agreement, confirming the framework's accuracy and reliability. This opens the way to practical uses ranging from pre-calibration of print settings, minimizing or even eliminating trial-and-error adjustments, to toolpath optimization for more advanced designs. Looking ahead, coupling the framework with simulations and sensor feedback could enable closed-loop digital twins for 3DCP, driving real-time process optimization, defect detection, and adaptive control of printing parameters.

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