The strength of ultra-low carbon maraging stainless steels can be significantly enhanced by precipitating nanoscale intermetallic secondary phases. Retained or reversed austenite in the steel can improve its toughness, which is key to achieving an ideal combination of strength and toughness. Ti and Al are often used as cost-effective strengthening elements in maraging stainless steels but the synergistic toughening and strengthening mechanisms of Ti and Al have not been studied. To investigate the synergistic toughening and strengthening mechanisms of Ti and Al in Co-free maraging stainless steels, this paper focuses on the microstructure and mechanical properties of three alloys: Fe-12Cr-11Ni-1.7Al-0.5Ti (Steel A), Fe-12Cr-11Ni-0.5Ti (Steel B), and Fe-12Cr-11Ni-1.7Al (Steel C). The impact of Ti and Al on the microstructure and mechanical properties was investigated using X-ray diffraction (XRD), high-resolution transmission electron microscopy (TEM), and thermodynamic simulations. The relationship between microstructure, strength, and toughness is also discussed. The results indicated that Steel A, containing both Al and Ti, exhibited the highest strength level after solution treatment at 900 °C, with an ultimate tensile strength reaching 1571 MPa after aging at 540 °C. This is attributed to the simultaneous precipitation of spherical β-NiAl and rod-shaped η-Ni₃Ti phases. Steel B, with only Ti, formed a significant amount of Ni-rich reversed austenite during aging, reducing its ultimate tensile strength to 1096 MPa. Steel C, with only Al, showed a high strength–toughness combination, which was achieved by forming dispersive nano-sized intermetallic precipitates of β-NiAl in the martensitic matrix with a slight amount of austenite. It is highlighted that Al has superior toughening and strengthening effects compared to Ti in the alloy system.

Silicon carbide-coated carbon fiber-reinforced carbon aerogel (SiC-C/CA) composites are ideal for high-temperature applications due to their ability to endure rapid temperature changes without losing structural integrity. However, assessing and optimizing the Thermal Shock Resistance (TSR) of these composites is challenging due to the complexities in measuring thermal and mechanical responses accurately under rapid fluctuations. Herein, we introduce a novel approach combining the cohesive finite element method (CFEM) with machine learning (ML) to address these challenges. The CFEM simulates crack initiation and propagation and captures mechanical behavior under thermal stress, while ML predicts TSR using simulation datasets, reducing the need for empirical trial-and-error processes. Our method achieves a prediction error for coating residual stress within 15.70% to 24.11% before and after thermal shock tests. Additionally, the ML model, developed to predict the average stiffness degradation factor of the SiC coating after three thermal shock cycles, achieves a coefficient of determination (R²) of 0.9171. This combined approach significantly improves the accuracy and efficiency of TSR assessment and can be extended to other coating materials, accelerating the development of high-temperature-resistant materials with optimized TSR for industrial applications.