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Thermo-resistant nanoalloys are a new class of materials that combine high-temperature refractory compounds (such as carbides, nitrides, borides, and oxides) with nanoscale particles of metals, ceramics, or carbon. These composites exhibit remarkable thermal stability and anti-ablation/oxidation properties, making them highly attractive for various high-temperature applications in aerospace, energy, and high-temperature manufacturing. Despite their potential, the fabrication of these materials is challenging due to their complex synthesis and processing. Many researchers have summarized the challenges and suggested solutions to produce high-density, superior physicochemical properties of nano refractory materials for specific applications. Thus, in view of these perspectives, the present review provides an overview of the production criteria, processing, and synthetic routes for producing high-temperature nano refractory material composites with exceptional thermal and anti-ablation/oxidation properties. The review also highlights the challenges encountered by researchers and their solutions for fabricating these materials. Potential applications of high temperature refractory materials are found in various industries, such as refractory ceramics, high-temperature components in wear resistant, neutron shielding, and high power-density microelectronics manufacturing to name a few.

Promptly predicting defects during an additive manufacturing process using only copious log data provides many advantages, albeit with computational limitations. We focus on predicting defects during electron beam melting with the black box nature of the manufacturing machine. For an accurate prediction of defects, which are rare ( < 2 % ), we extract temporal information to track abnormalities and formulate a feature selection algorithm that maximizes the expected value of a cost-sensitive accuracy. Correct identification of features responsible for the defects increases predictive power and informs manufacturers of potential corrective/preventive actions for process improvement. We solve the feature selection through resampling strategies integrated with ensemble procedures to handle data uncertainty and imbalance. Exploiting data uncertainty in our search leads to finding robust features with consistent predictive power. Our proposed methodology shows a 43% improvement in predicting defects (recall) without losing precision. Beyond additive manufacturing, this methodology has general application for rare-event prediction and imbalanced datasets. [ABSTRACT FROM AUTHOR]