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Machine learning for 3D printing

This page provides open-source machine learning codes and tools for 3D printing applications, developed by members of the Department of Civil and Environmental Engineering (DICA) at Politecnico di Milano, including Prof. Massimiliano Cremonesi, Giacomo Rizzieri and Federico Lanteri.

 

ShapeGen3DCP

ShapeGen3DCP  is a fast and user-friendly open-source tool to predict the cross-sectional geometry of the printed layers in 3D Concrete Printing (3DCP) based on material and process parameters. The tool uses machine learning models trained on computational fluid dynamics simulations to provide rapid predictions, enabling optimization of printing parameters for desired outcomes.

If you use ShapeGen3DCP in your work (e.g., books, articles, reports), please cite:
G. Rizzieri, F. Lanteri, L. Ferrara, M. Cremonesi, “ShapeGen3DCP: A deep learning framework for layer shape prediction in 3D concrete printing”, Computers & Structures 323 (2026) 108142. doi:10.1016/j.compstruc.2026.108142