Infrared thermography (IRT) is a well-established and well-documented nondestructive evaluation (NDE) technique which has been proved as one of the critical assessment tools providing not only qualitative but also quantitative results useful for various applications. Even though many post processing methodologies have been used for thermal imaging analysis, there is still a need for a methodology that could possibly reduce the noise, improve the Signal to Noise Ratio (SNR) and focus on a specific area of interest reconstructing automatically the thermal image. This work deals with fine-tuning the IRT method in order to assess the detectability of damage in composite materials.
In recent years, the damage assessment by means of Laser Doppler Vibrometry (LDV) has become very attractive as it provides non-contact, non-destructive, accurate and improved evaluation of advanced materials. This study deals with the development of advanced software based on LabVIEW in order accurate and automated measurements of acoustic activity to be achieved. Furthermore, this automated method was applied for damage detection in aluminum 1050 Η16 undergone cyclic mechanical loading. LDV was used to measure the amplitude of a Rayleigh surface wave propagating in aluminium specimens. Rayleigh waves are experimentally generated with a piezoelectric transducer and detected by LDV. The proposed measurement technique is used to assess the damage and its evolution, in terms of the increasing amplitude of Rayleigh wave, in 1050 H16 specimens under cyclic mechanical loading. In addition, the reduction in the Rayleigh wave velocity it depends on ultimate fatigue strength of material. The development of this process allows the automated, improved and detailed damage assessment of composite materials.
Scanning acoustic microscopy uses a focused acoustic beam to investigate local elastic properties on the surface of a material. The measurement is based on the difference in propagation time between the direct reflection and the Rayleigh wave. This work deals with the development of a fully automated acoustic microscopy method in order to determine the near-surface elastic property and map sub-surface features in metallic and composite materials. This method allows for the detection and analysis of Rayleigh waves, which are sensitive to subtle changes in the material’s local elasticity. Via this process, the periodicity of the V<sub>(Z)</sub> curve can be initially assessed and the local Rayleigh velocity of the material is determined. In this work, the automated acoustic microscopy method was applied for the assessment of aluminum and Al-SiC metal matrix composites.