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Aluminum and A356 alloy foam castings are produced using a melt-foaming method. Prior to foaming, the melt is modified with nano-sized particles (SiC, TiN, or Al2O3). The nano-sized particles are mixed with micro-sized Al particles, which are ultrasonically treated and hot-extruded. Thus, the so-called "modifying nano-composition" is obtained. The resulting compositions are introduced into the melt of the Al foam at the following mass concentrations of nanoparticles: SiC: 0.038 wt. %; TiN: 0.045 wt. %; and Al2O3: 0.046 wt. %. For the A356 foam, we use the following concentrations: SiC: 0.039 wt. %; TiN: 0.052 wt. %; and Al2O3: 0.086 wt. %. The macrostructure of the foam castings is investigated by CT scanning and 3D analysis. The pore size distributions and accumulative fraction dependencies are determined for all samples. The microstructure of the foam castings is investigated by SEM-EDS analysis. The results confirmed the presence of individual nano-sized particles, as well as clusters of particles in foam walls. The conducted compression tests show a significant increase in the plateau stress (up to 237%) of the modified aluminum foam castings compared to non-modified castings. However, a similar effect of the nano-compositions on A356 alloy foam castings is not observed. The obtained results show that the above-indicated concentrations of nanoparticles can positively influence the mechanical properties of aluminum foam castings. The novelty of the current study is two-fold: (1) such low concentrations of added nanoparticles have never been used before to alter Al foam's properties, and (2) an original method of introducing the nanoparticles into the melt is applied in the form of nano-compositions. [ABSTRACT FROM AUTHOR]

This study investigates the fabrication, wear characterization, and coefficient of friction (COF) prediction of open-cell AlSn6Cu–Al2O3 composites obtained by a liquid-state processing technique. Focusing on wear behavior under varying loads using the pin-on-disk method, this research characterizes microstructure and phase composition via SEM, EDS, and XRD analyses. A novel aspect of this research is the application of an LSTM recurrent neural network model for the fast and accurate prediction of the COF of the composites, eliminating the need for extensive experimental work. Additionally, feature importance analysis using Random Forest regressors is conducted to ascertain the relative contribution of each input variable to the output variable, enhancing our understanding of the wear mechanisms in these materials. The results demonstrate the effectiveness of the composite's reinforcement in improving wear resistance, highlighting the critical role of mechanical stress and the reinforcement's hardness in the wear process. The quantitative findings related to the wear behavior include a mass-wear reduction in the open-cell AlSn6Cu–Al2O3 composite from 8.05 mg to 1.90 mg at 50 N and a decrease from 17.55 mg to 8.10 mg at 100 N, demonstrating the Al2O3 particles' effectiveness in improving wear resistance under different loads. [ABSTRACT FROM AUTHOR]