Open-cell Al foams were produced by the replication casting technique in three different pore sizes. All produced foams were physically characterized, determining their relative density, porosity, and pores per inch, as well as their mean pore surface area and diameter. Permeability tests were carried out by means of the injection of a highly pressurized gasoline additive at room temperature and 200 °C, at pressures of up to 25,000 psi. The structural capacity of the studied specimens to conduct fluids at these critical experimental conditions was assessed by means of compression tests in order to determine their mechanical properties after the permeability tests, e.g., energy absorption capacity, Young’s modulus, and plateau stress. It was found that the produced open-cell Al foams were able of conducting the gasoline additive at critical flow conditions of pressure and temperature, without suffering important physical nor structural damage. Graphic

Commercially pure aluminum (Al) was refined through the addition of the Al-5Ti-0.25C master alloy, resulting in the formation of Al₃Ti and TiC phases, which serve as refining agents. Open-cell metallic foams were successfully produced using the replication casting technique, with pore sizes ranging from 1.00 to 3.35 mm. For the infiltration process, refined aluminum was used, while unrefined aluminum served as a baseline reference. The resultant foams underwent multiple annealing cycles at 480 °C, with the most refined and homogeneous microstructure observed after 504 h. Comprehensive microstructural characterization was conducted utilizing scanning electron microscopy and optical microscopy. Additionally, uniaxial compression tests were performed to generate stress–strain profiles for the foams, facilitating an assessment of their energy absorption capacity. The findings indicated an enhancement in energy absorption capacity by a factor of 2.4 to 3, which can be attributed to the incorporation of Al-5Ti-0.25C and the subsequent annealing process.

Among a broad range of promising applications, the use of cellular solids as lightweight structural components is an important field of research that requires reliable predictions of their stiffness and strength. Predictive and general models should not depend on extensive parameter-fitting experiments and should not rely on computationally intensive numerical calculations for each new set of geometric parameters and loading conditions. An overview of models for 2D, 2.5D, and three-dimensional structures will be presented. Most 2D and 2.5D models neglect out-of-plane behaviour and the face sheets used in sandwich panels. 3D studies, mainly by finite element models (FEMs), are often limited to a narrow set of geometries and simple loading conditions. Elastic anisotropy is well covered, but calculating yield surfaces remains a challenge. Simplified models based on structural mechanics are rare and often limited in scope. They offer a flexible, computationally efficient approach for simulating truss-based materials. For more advanced designs, parameter-based FEMs must be developed for any loading condition to facilitate the generalised incorporation of 3D cellular solids in mechanical design. Artificial intelligence and machine learning are promising approaches for making optimal use of experimental and FEM results across multidimensional parameter spaces.