Evaluation of fatigue crack growth rates and fracture toughness in a selective laser-melted Ti-5.6Al-3.8V alloy with optimized microstructure after heat treatment

Abstract

Selective Laser Melting (SLM) has emerged as a promising additive manufacturing technology for fabricating intricate titanium alloy components, drawing significant attention to the comprehensive mechanical properties of the resulting alloys. This work investigates the fatigue crack propagation and fracture behavior of the SLM Ti-5.6Al-3.8V alloy. The fatigue crack growth rate and fracture toughness of this alloy were further analyzed, which are comparable to those of the forged Ti-6Al-4V alloy and surpass those reported for other SLM Ti-6Al-4V alloys. Combining the characterization of the fracture morphology and microstructural characteristics, it was found that the microstructure of fine "β columnar grains + α laths" promotes the formation of jagged α cracks and crack deflection during the macroscopic and smooth propagation of fatigue cracks, thereby helping to delay cracks. Additionally, the influence of the microstructure on crack propagation in this alloy is evident in the deformed α-laths and the distribution of micropores to varying degrees formed during plastic deformation. These phenomena help to eliminate the stress concentration at the crack tip, thereby hindering the rapid fracture after the crack instability propagation. Furthermore, X-ray diffraction (XRD) detected αʺ phase diffraction peaks in both as-deposited and heat-treated SLM Ti-5.6Al-3.8V alloy samples, which disappeared after test, indicating martensite phase transformation during fatigue crack propagation and fracture processes. These findings underscore the exceptional mechanical properties of SLM Ti-5.6Al-3.8V alloy and highlight the significance of microstructural features and martensite phase transformation in influencing fatigue crack propagation and fracture behavior. This work contributes to a deeper understanding of the mechanical properties of SLM titanium alloy and provides insights for optimizing properties for various engineering applications.