This article presents a review of current methods for production of metallic open-cell porous materials through space holders. The methods are divided into two major groups: on the basis of sintering and using liquid phase processing. Details about technologies are given, and their relations to structure parameters of obtained materials are discussed. Methods with 11 different space holders are described. The space holders could be metallic or nonmetallic (organic and inorganic) materials which could be leached or burned depending on removal technique. It is concluded that the flexible application of different space holders offers opportunities for obtaining large variety of metallic porous structures. A new line of development should be elaboration of complex techniques for production of porous structure with graded pore size and/or porosity which will meet various engineering requirements and will open new possibilities for applications as functional and structural elements. The next part of this work is devoted to the structure, the properties, and application of the open-cells porous materials obtained through space holders.

This work presents an overview of structural characteristics and basic mechanical properties of the open-cell metallic foams obtained by different space-holder methods, which are discussed in Part I of the same review. The presentation is arranged with respect to foam material, and the structure and properties are compared for different space holders and production techniques. In order to have more clear information for the structures obtained and their relation with production techniques, many images are provided and discussed. Compressive behavior of the foams is shown, and stress–strain curves are analyzed with respect to the energy absorption characteristics. The analysis are made on the basis of different porosities and processing parameters. Some applications of the open-cell metallic foams are discussed in the end of the article.

The study presents a novel hybrid deep learning and ensemble learning (DL-EL) model to predict the effects of chemical composition and thermo-mechanical processing on the properties of Cu-Ni-Si alloys. The model integrates various input parameters like chemical composition and thermo-mechanical processing parameters and aims to predict key output properties such as mechanical properties and electrical conductivity. This study addresses gaps in existing research by providing a comprehensive model for Cu-Ni-Si alloys and integrating thermo-mechanical processing parameters with chemical composition in the predictive model. The research demonstrates the model's superior predictive performance, with near-perfect R 2 values on both training and test sets. The hybrid DL-EL model was compared with three other machine learning models and its efficacy was assessed using R 2 value, offering new insights into the performance of these alloys. A feature importance analysis was conducted to identify the most influential features of the model. This work contributes to the material science field by providing insights into optimizing alloy composition and processing, leveraging machine learning to enhance material design.

The study investigates the effects of graphene nanoplatelets (GNPs) on the tribological properties of aluminum-based nanocomposites, both annealed after extrusion and non-annealed. It also examines the role of nanosized Al₄C₃ (aluminum carbide), which forms in the annealed Al/GNPs nanocomposite, on the tribological performance of the nanocomposites. The nanocomposites were fabricated using the powder metallurgy method. The microstructure of the composite materials was characterized using SEM, EDS, XRD and TEM techniques. The coefficient of friction (CF) and mass wear of the composites were measured using a pin-on-disk test under dry sliding friction conditions. The results showed that adding GNPs increased the coefficient of friction (CF) of the nanocomposites by up to 44% at 0.1 wt.% GNP, but the CF decreased by 15% at 1.1 wt.% GNP. The optimal concentration of GNPs for minimizing the CF and mass wear of Al-based nanocomposites was 0.1 wt.%. Additionally, the presence of Al₄C₃ in the annealed Al/GNP nanocomposite had a positive effect on the CF at low GNP concentrations, with a 38% increases at 0.1 wt.% GNP, but this effect diminished as the GNP concentration increased. The study also found that the mass wear of the nanocomposites increased with the GNP concentration, with a 46% increase in the mass wear from 0.1 wt.% GNP to 0.5 wt.% GNP and a 202% increase from 0.1 wt.% GNP to 1.1 wt.% GNP. The presence of Al₄C₃ also affected the mass wear, with the effect diminishing as the GNP concentration increased. The study observed an increase in the mass wear with the increase in the GNP concentrations, but the mass wear of the annealed Al/GNPs with 1.1 wt.% GNP and Al₄C₃ was 52% lower than the Al composite with 1.1 wt.%. Overall, this study provides insights into the role of GNPs and Al₄C₃ on the tribological performance of aluminum-based nanocomposites.