The Ni-hard alloys white-cast irons are generally used for high wear work. Among them, those with better impact resistance because of its low carbon content compared to the rest of the family, are studied in this paper. One experimental technique of characterizing the metallic materials is the microstructural study. Several metallographic attacks intended to reveal qualitatively each microconstituent that forms the alloy, as well as the segregation and solidification structure of casting, are studied in this article. The use of color metallography is fundamental in this case to distinguish clearly the microconstituents. The main objective of this paper is to propose a series of attacks that identify each one of the microconstituents present in the alloy that has not been reported up to date.

A novel and cost-effective methodology is introduced for the precise prediction of the melting time of metals and alloys in a 700 W domestic microwave oven, using a hybrid SiC–graphite susceptor to ensure efficient heating without direct interaction with microwaves. The study includes experimental trials with multiple alloys (Sn–Bi, Zn, Zamak, and Al–Si, among others) and variable masses, whose results made it possible to construct a dimensionless model, trained with XGBoost on easily measurable thermophysical properties (specific heat, density, thermal conductivity, mass, and melting temperature). The model achieves high accuracy, with a relative error below 5%, and metrics of MAE = 4.8 s, RMSE = 6.1 s, and R² = 0.9996. The generalization of the model to different microwave powers (600–1100 W) is also validated through analytical adjustment, without the need for additional experiments. The proposal is implemented as a Python application with a graphical interface, suitable for any academic or teaching laboratory, and its performance is compared with classical models. This approach effectively contributes to the democratization of thermal testing of metals in educational and research settings with limited resources, providing thermodynamic rigor and advanced artificial intelligence tools.