Unraveling the temperature-independent damage modes of the TGO under high-temperature tensile loading through real-time acoustic emission detection combined with digital image correlation
Source abstract
Abstract
Thermally grown oxide (TGO) plays a crucial role in thermal barrier coatings. However, due to the absence of real-time detection methods, the damage process of TGO remains poorly understood. In this study, the damage evolution and fracture behavior within the TGO and at the TGO/bond coating interface are investigated under high-temperature tensile loading, coupled with real-time acoustic emission (AE) and digital image correlation (DIC). Based on the characteristic frequency bands of AE signals, three distinct temperature-independent damage modes are identified: surface vertical cracks in TGO (210-290 kHz), interfacial cracks at the TGO/bond coating interface (310-390 kHz), and substrate deformation (100-190 kHz). After extracting the corresponding characteristic frequency spectra through Fourier transform, it is observed that within the temperature range of 400-1000°C, although the signal waveforms and spectral shapes vary with temperature, their characteristic frequency ranges and dominant frequencies remain consistent: substrate deformation (type B) ranges from 0.13 to 0.17 MHz with a dominant frequency of 0.15 MHz; TGO surface cracks (type C) range from 0.21 to 0.26 MHz with a dominant frequency of 0.24 MHz; and TGO/bond coating interfacial cracks (type D) range from 0.31 to 0.37 MHz with a dominant frequency of 0.33 MHz. The analysis results indicate a clear correspondence between the characteristic frequency spectrum range and the characteristic frequency band of the AE signals. This establishes a solid frequency-domain basis for identifying high-temperature damage modes in TGO.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: High-Temperature Coating Behaviors · Corrosion Behavior and Inhibition · Advanced ceramic materials synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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