The usage of ultrasound in imaging is widely used for material characterisation, alongside other defect-oriented tasks such as defect detection and localisation. In metallic materials factors such as grain size, orientation and shape are different useful properties to identify but come with challenges to estimate. For example, to measure the mean grain size from the backscattering intensity, it is usually assumed that the scattering is weak, so the interaction of ultrasonic waves with the microstructure may be described by the single scattering approximation. Additionally, conventional ultrasonic inversion methodologies require knowledge of elastic constants of the crystallites of the tested material. In this case measurements can be predicted by well-established models. However, experimental verification of the single scattering assumption is not possible in the traditional single probe measurement framework, and material properties of engineering alloys may not always be known. To address these limitations, in this paper a method of estimating the average grain size using full-matrix capture (FMC) data from a 1D ultrasonic array is developed. The method is based on an ultrasonic array grain scattering model and allows to estimate the grain scattering coefficient (Figure-of-Merit, FoM) at a range of frequencies directly from ultrasonic total focusing method (TFM) images. The technique is applied experimentally on several copper samples with different grain sizes and good agreement with optical microscopy results is found.

A critical point in the manufacture of wind turbine bearings is ensuring surface hardening while their cores remain in the original structural condition. Surface hardness and case-depth measurements are the most important parameters for quality monitoring of surface-hardened steel products. One of the techniques commonly used to measure the depth of hardening is ultrasonic backscatter.

The hardening process modifies the crystallographic structure of the material, creating a hardened layer with fine grains on the surface, while the core retains a coarse-grained structure. Given this condition, the backscattering technique, using frequencies between 10 MHz and 20 MHz, is ideal for detecting the depth of the hardened layer. These frequencies are well-suited for distinguishing reflections generated at the interface between the hardened layer and the core, taking advantage of the increased wave scattering in the coarse-grained core compared to the fine-grained surface layer.

The most widely used inspection technique today relies on generating shear waves with single-element transducers. Although this method delivers good results in many inspections, it faces several challenges:

• Strong dependence on operator experience: Accurate interpretation requires a high level of expertise, which can vary significantly between operators.
• High reliance on the calibration process: The inspection's accuracy heavily depends on the quality and precision of calibration, making it a crucial step.
• Discrepancy between ultrasonic and destructive measurements: Particularly noticeable in areas with a gradual transition zone, leading to differences between ultrasonic and destructive testing results.

The use of advanced phased array technologies can significantly address these challenges. In this work, we explore how advanced techniques such as Plane Wave Imaging (PWI) and Total Focusing Method (TFM) can be used to image the backscattering pattern in the hardness layer's transition zone and reduce dependence on operator expertise, utilizing the TPAC device Explorer 64/128 and SW Prelude 5. Transducer design and UT settings were approached using BeamTool 11 to define the grid and sensitivity area. This approach allows a point-by-point evaluation of a T-Scan view, contrasting with traditional A-scan analysis, simplifying the detection of the transition zone's position and enabling the use of tools like spatial averaging to increase measurement precision.

Additionally, we compare different reconstruction methods, including DAS, PCI, pDAS, and IPCI, which are adapted to an automatic depth detection algorithm to enhance accuracy. Tests are conducted on various reference samples where hardness was destructively characterized. The results obtained from mock-ups and actual components are summarized in a comparative table.