CMMI-EPSRC: Quantitative Characterization of Mission Critical Microstructures of Engineering Metals with Diffusive Ultrasound

Catastrophic failures of components (e.g. in aero engines) brutally expose the limitations of the existing industrial capability to quantitatively characterize mission-critical engineering metals. These metals are polycrystalline, with physical and structural properties of the crystallites typically anisotropic; thus, many vital properties of the finished components, including strength, fatigue life, and creep and corrosion resistance, are strongly dependent on the volumetric grain microstructures, such as the grain size, shape and clusters. Yet these details are very difficult to measure. Current standard practice is restricted to destructive, two-dimensional sections of sacrificial samples, which remains laborious, costly and inaccurate. Ultrasound provides an accessible and non-destructive way to evaluate the fitness-for-service of components throughout the volume. However, it is subject to convoluted effects from sample geometries, microstructures and preferred crystallographic orientations (texture) and, despite decades of research, there lacks model-supported quantitative linkages to extract the microstructural characteristics reliably from ultrasound.
This proposal seeks to establish the ultrasonic diffuse wave field (DWF) method to fulfil the need for such volumetric characterization. The DWF is fundamentally an end-result of the microstructures, created by multiple scattering of wave energy at the boundaries of grain inhomogeneities. The most important physical feature of the DWF is that a cross-correlation of the signals recorded at two arbitrary points delivers the mean Green's function, which is equivalent to the impulse response between the points, and intrinsically carries information of the scattering history and the critical microstructures. Importantly, the DWF method's sensitivity to these microstructures is not limited by complex sample geometries; therefore, it could take full advantages of state-of-the-art equipment (e.g. laser ultrasound or phased arrays) for more flexible modalities, e.g. measuring without physical contact, at elevated temperatures, and at manufacturing stages from raw material to finished components.
To realise these potentials, PI Lan will develop the experimental means to measure the elastodynamic Green's tensor from localised inspections of the DWF. The output will capitalise on a recent disruptive scientific progress to measure volumetric texture from ultrasonic wave speeds, which enables the effects of texture on ultrasound to be de-coupled from microstructures. Meanwhile, PI Kube will develop theoretical models to uncover new physical understanding of the DWFs in relation to microstructural heterogeneities seen in modern metallic alloys. This will develop recent major advances on multiple scattering and radiative transfer theories for the tensorial elastodynamic form. The PIs will also join forces in computational modelling of the dynamic evolution of the incoherent diffuse field, utilising the leading simulation capabilities at Imperial. These aspects of research will interact with and facilitate each other, and will all be combined in the development of a quantitative methodology for inverse microstructure characterization.
The advanced experimental capability, fundamental physical understanding, and quantitative inversion, which are targeted by our project, will allow the DWF method to transit into manufacturing lines and routine in-service non-destructive testing protocols. Raw materials and real components could be continuously and non-destructively characterized throughout the production and service stages, to reduce destructive testing, lower manufacturing waste, and, most importantly, to assure performance and safety. The prospective connections to materials-by-design, materials genome, and process-structure-property-performance are especially exciting. The proposal is submitted as an Aligned Project of of the UK Research Centre in NDE (RCNDE).