Evaluating and understanding the effect of manufacturing processes on the creep performance in superalloys poses a significant challenge due to the intricate composition involved. This study presents a machine‐learning strategy capable of evaluating the effect of the heat treatment process on the creep performance of superalloys and predicting creep rupture life with high accuracy. This approach integrates classification and regression models with domain‐specific knowledge. The physical constraints lead to significantly enhanced prediction accuracy of the classification and regression models. Moreover, the heat treatment process is evaluated as the most important descriptor by integrating machine learning with superalloy creep theory. The heat treatment design of Waspaloy alloy is used as the experimental validation. The improved heat treatment leads to a significant enhancement in creep performance (5.5 times higher than the previous study). The research provides novel insights for enhancing the precision of predicting creep rupture life in superalloys, with the potential to broaden its applicability to the study of the effects of heat treatment processes on other properties. Furthermore, it offers auxiliary support for the utilization of machine learning in the design of heat treatment processes of superalloys.

Cold expansion technology has been widely used in aviation industries as an effective method of improving the fatigue performance of fastener holes. It can improve the fatigue life several times over without adding weight, meeting the growing demand for lightweight and durable aircraft structures. In recent years, it has been extensively studied through extensive experiments and finite element simulations to analyze the residual stresses around the fastener hole. Appropriate process parameters lead to the generation of beneficial residual stresses that influence the material microstructure, thereby improving the fatigue life of the component. This paper summarized factors influencing residual stresses in cold expansion and their effects on fatigue life, and the strengthening mechanism, parameter optimization, and effect of anti-fatigue are discussed from the point of view of the residual stress and microstructure. The development of new cold expansion technologies and the research directions that can realize anti-fatigue technology efficiently are proposed.

Machine learning has emerged as a powerful tool for predicting material properties due to its efficiency and accuracy. However, challenges related to data integrity, particularly the presence of ambiguous data, have limited its broad application. In this work, a novel strategy is proposed that integrates partial label learning and transfer learning to accurately address ambiguous compositional data in predicting the fatigue performance of superalloys. Subsequently, key microstructural features are enriched through thermodynamic calculations based on the composition data, enhancing model interpretability by revealing composition‐microstructure‐property relationships. This approach not only achieves superior predictive accuracy but also exhibits robust generalization across experimental validation. Given the widespread presence of ambiguous data, this framework holds significant potential for broader applications in materials science.

Precipitation coarsening characteristics at elevated temperature have a powerful influence on the thermodynamics properties of TiB₂ particles reinforced aluminium-copper matrix composites (PRAMCs), aging precipitation of θ-Al₂Cu is stable at room temperature, but coarsening occurs at elevated temperature, In this paper, Al-4.5Cu matrix composites with different content TiB₂ are prepared via the molten salt reaction to evaluate the microstructures transformation and the related elevated temperature mechanical properties. Experimental results show that yield strength (YS) and ultimate tensile strength (UTS) of TiB₂/Al-4.5Cu composites increase with the augment of TiB₂ mass fraction, while the elongation decreases gradually both at elevated-temperature of 453k and 493k. The analysis suggests that the remarkable α-Al grain refinement along with pinning effect of TiB₂ particles has a great effect on the grain boundary sliding and migration at high temperature. Additionally, interface debonding also affects the damage and fracture of the composites.