TY - GEN
T1 - A Multimaterial Topology Optimization Approach for Large-Scale Additive Manufacturing of Spinodal Material Architectures
AU - Chiozzi, Andrea
AU - Nale, Andrea
AU - Senhora, Fernando V.
AU - H. Paulino, Glaucio
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Topology optimization has received growing attention during the last decades in several engineering areas, and such interest has increased after the diffusion of additive manufacturing technologies. However, only in recent years applications of topology optimization have begun to spread in the field of civil engineering. In this contribution, we present a synergistic methodology to design large-scale 3D-printed structures based on a multimaterial topology optimization formulation, which leads to the realization of three-dimensional hierarchical architectures with spatially oriented non-periodic spinodal microstructures. The inherent characteristics of these unstructured architectures allow the design of optimized layouts with smooth transitions of spinodal material classes, accounting for varying porosity and orientation. The design and manufacturing processes are bridged by a topology optimization formulation, in which the iterative process preserves the macroscale continuity, while the microstructural topological space is optimized by a suitable distribution of multiple spinodal architected materials. The approach is intertwined with the development of an innovative large-scale water jetting powder-bed 3D printing technology, which makes use of aggregates obtained from powdered stone-like materials and magnesium-based binders. The scalability of the approach is illustrated by means of numerical examples and the realization of physical 3D printed samples.
AB - Topology optimization has received growing attention during the last decades in several engineering areas, and such interest has increased after the diffusion of additive manufacturing technologies. However, only in recent years applications of topology optimization have begun to spread in the field of civil engineering. In this contribution, we present a synergistic methodology to design large-scale 3D-printed structures based on a multimaterial topology optimization formulation, which leads to the realization of three-dimensional hierarchical architectures with spatially oriented non-periodic spinodal microstructures. The inherent characteristics of these unstructured architectures allow the design of optimized layouts with smooth transitions of spinodal material classes, accounting for varying porosity and orientation. The design and manufacturing processes are bridged by a topology optimization formulation, in which the iterative process preserves the macroscale continuity, while the microstructural topological space is optimized by a suitable distribution of multiple spinodal architected materials. The approach is intertwined with the development of an innovative large-scale water jetting powder-bed 3D printing technology, which makes use of aggregates obtained from powdered stone-like materials and magnesium-based binders. The scalability of the approach is illustrated by means of numerical examples and the realization of physical 3D printed samples.
KW - Large-Scale Additive Manufacturing
KW - Topology-Optimization
UR - https://www.scopus.com/pages/publications/105039123385
UR - https://www.scopus.com/pages/publications/105039123385#tab=citedBy
U2 - 10.1007/978-3-032-17231-0_111
DO - 10.1007/978-3-032-17231-0_111
M3 - Conference contribution
AN - SCOPUS:105039123385
SN - 9783032172303
T3 - Lecture Notes in Mechanical Engineering
SP - 883
EP - 889
BT - Proceedings of 26th AIMETA Conference 2024
A2 - Fraldi, Massimiliano
A2 - Rosati, Luciano
A2 - Marchioli, Cristian
A2 - Naso, Maria G.
A2 - Parenti Castelli, Vincenzo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th Conference of the Italian Association of Theoretical and Applied Mechanics, AIMETA 2024
Y2 - 2 September 2024 through 6 September 2024
ER -