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Potential energy of an alloy is an essential indicator for evaluating the stability of the structure in predicting new materials. Therefore, how to calculate the potential energy in material design has become an inevitable problem. While first-principles calculations can provide chemical accuracy for arbitrary atomic arrangements, they are prohibitive in terms of computational effort and time. To enable atomistic-level simulations of both the processing and performance of Aluminum alloys, neural network potential was proposed to predict the binding energy of vacancy-containing aluminum alloys in a highly accurate state. This method combined first-principles calculations and machine learning techniques to explore the intrinsic link between solid solution structure and binding energies. In this study, four binary alloys (aluminum-silicon, aluminum- zirconium, aluminum-magnesium and aluminum-titanium alloys) were investigated. The mean squared errors were used to quantify the quality of the neural network potential models and it was found that the trained model is more stable and exhibits high accuracy for energy prediction. The Monte Carlo simulation results show that using this neural network potential successfully simulated aging process of aluminum alloys, and the neural network potential can be much faster than first-principles calculations, even with high accuracy.

Hard chromium coating is mainly used to improve the surface performance of pistons, brake disks and gears. In the future, better abrasion resistance and better corrosion resistance of hard Cr coating are demanded. Co-deposition of ceramic particles in Cr coating improved these characteristics. The Cr-ZrO2 composite coating is one of the prospective candidates for improvements of mechanical properties. In the present work, the measurement of polarization behavior of low carbon steel in CrO3-H2SO4 electrolyte with ZrO2 addition was conducted to investigate corrosion resistance. Microstructure of coating surface was observed with Electron Prove Micro Analyzer (EPMA) and Transmission Electron Microscope (TEM). Vickers hardness of coating surface was measured. The addition of ZrO2 had no effect on pH. Electrolyte was still strong acid. The shape of polarization curve was changed by the addition of ZrO2 particles. Polarization point was shifted higher by the addition of ZrO2 particles. The corrosion resistance was improved by the addition of ZrO2 particles. ZrO2 particles made the reduction reaction slowly. Coating surface consisted of Cr nano-crystal whose average size was 32.7 nm. Aggregation of ZrO2 particles was not observed in the resolution of EPMA. Addition of ZrO2 particles improved Vickers hardness of coating surface.

A new process is proposed to fabricate an aluminum alloy matrix composite dispersed with intermetallic compound particles by using the reaction between porous nickel and molten aluminum alloy. The intermetallic compound particles reinforced aluminum alloy matrix composite was manufactured with the infiltration-reaction method. Four different specific surface areas of porous nickel were used to fabricate the composites and then the porosity inside composites were investigated. Porous nickel reacted with molten aluminum alloy at 973 K, and the intermetallic compound, Al3Ni was generated on the surface of the porous nickel. The generated intermetallic compound Al3Ni, was delaminated according to the difference of thermal expiation coefficient with nickel. And the intermetallic compounds moves in the direction of aluminum matrix. The area fraction of the intermetallic compounds increased with the increasing specific surface area of porous nickel. In addition, the hardness of composite increased by the increasing specific surface area of porous nickel.

The goal of the present work was developing the software for calculating the electrical conductivity of aluminum-based particle-dispersed composites by using the actual microstructure images and by performing conduction simulations. However, the electrical conductivity calculated using the microstructure image was supposed to be not equivalent to the electrical conductivity measured by using any of the existing experimental methods, because the microstructure image is a two-dimensional source of information, although the experimental conductivity is obtained from three-dimensional microstructures. Therefore, the statistical relationship between three- and two-dimensional electrical conductivities was investigated by performing the conduction simulations. The electrical potential distribution in the steady state was calculated by using the finite volume method, and the electrical conductivity of the composites was evaluated. The average electrical conductivity of two-dimensional cross-sections was lower than that of a three-dimensional cell, and the difference increased with increasing volume fraction. When the difference between the matrix and reinforcement electrical conductivities was small, the difference between the two- and three-dimensional electrical conductivities was small as well. The difference between the two- and three-dimensional electrical conductivities was constant for all volume fractions even when dispersibility changed. A correction for converting the two-dimensional electrical conductivity to the three-dimensional electrical conductivity was suggested. The electrical conductivity of Al/TiB2 was evaluated from the actual microstructure image by performing the conduction simulation.