Despite decades of research and development of Alnico alloys, significant uncertainties in the underlying structure-property relationships remain. Here, we report on the effects of Ti on the Alnico microstructure and nanostructure, and the corresponding influence on magnetic properties. We show that Ti fosters the conditions resulting in the formation of Cu-Ni-rich bridges in the α ₁ phases between the α ₂ phases. For Alnico containing Ti, a typical chessboardlike morphology with Cu-Ni-rich bridges is observed, whereas in the absence of Ti, the α ₁ phases connect to each other readily, especially with a high Co concentration, and a mazelike morphology with Cu-rich white-plate precipitates rather than Cu-rich bridges is observed. Furthermore, in Alnico containing Ti, an inhomogeneous distribution of Ni is found in the α ₂ phases, including loops with high Ni concentration surrounding the α ₁ phase and high concentrations in the bridges as well. An increase in the Cu concentration is also observed in the loops around the α ₁ phases (Ni-Cu loops), and direct contact between the Cu-Ni-rich bridges and the Ni-Cu loops is observed in lieu of direct contact between the bridges and the α ₁ phases. We also observe that the bridges are not perfectly round but ellipsoidal, with the long axis along the connection of two adjacent α ₁ phases. The energy-dispersive X-ray spectroscopy line scans of the bridges shows that two types of Cu-Ni-rich bridges exist: those with more Cu than Ni and those with more Ni than Cu. A 3-D model is presented that explains the conditions and process of bridge formation, consistent with the observed composition distributions.

Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.