Monday, 23 June 2025 will feature a Day of Tutorials. These informative, and interactive, presentations are intended to focus on fundamental issues within NDE. Extended in length (50-60 minutes), there will also be 10-20 minutes allocated to each tutorial for Q&A.
* Additional Registration Required
The integration of ultrasonic guided waves and time reversal (TR) methods has aroused significant interest within the nondestructive evaluation (NDE) and structural health monitoring (SHM) communities. This combination offers a powerful and baseline-free tool for damage detection and characterization. In this tutorial, we will explore the fundamentals and advancements of guided wave TR imaging techniques. The tutorial will begin by introducing the traditional TR concept and its capability to compensate for Lamb wave dispersive features. Then, we will move to several innovative variations of TR methods, such as modified time reversal and virtual time reversal (VTR). The VTR algorithm only needs a typical pitch-catch active sensing signal, and the TR procedure is replaced by computerized virtual signal operations. A focus will be placed on an enhanced Lamb wave VTR technique, which addresses the challenge of transducer tuning effects in the application of TR methods. By compensating with the transducer transfer function, the time-reversibility for multi-modal and dispersive Lamb wave modes is significantly improved. The tutorial will finish with some additional applications of guided wave TR techniques, highlighting their versatility and potential to advance the NDE and SHM fields.
Spatially Resolved Acoustic Spectroscopy (SRAS) is an acoustic microscopy technique capable of imaging the microstructure of materials. By combining multiple measurements with theoretical predictions of surface acoustic wave (SAW) velocities, SRAS can determine the crystallographic orientation of individual grains. More recently, the technique has been further extended to enable the determination of single-crystal elasticity.
This tutorial will introduce the fundamentals of SRAS, focusing on its applications in grain imaging across a variety of prepared and industrially relevant surface finishes. The technique operates by measuring the velocity of SAWs through the acoustic spectrum. These waves are generated using a laser to create a line-patterned grating and are detected by another laser at a nearby point. Measuring velocity via the acoustic spectrum, rather than time-of-flight, offers several practical advantages, including enhanced robustness, faster processing, and superior spatial resolution. By employing forward models to calculate the expected SAW velocities, SRAS can determine the crystallographic orientation of grains in materials with various crystal symmetries, including cubic, hexagonal, and tetragonal systems. This tutorial will showcase results across these symmetries and highlight recent advancements that enable the determination of single-crystal elasticity matrices. The tutorial will conclude with a discussion of future applications, including the potential for real-time monitoring of material processes.
Electromagnetic (EM) inverse scattering (IS) methods are pivotal for a wide range of interesting engineering applications, including biomedical diagnostics, through‐the‐wall imaging, structural health monitoring, and non‐destructive testing and evaluation (NDT/NDE). As a matter of fact, such methods allow for the retrieval of both qualitative (e.g., location and shape) and quantitative(e.g., material properties) features of unknown targets within an inaccessible domain/host structure. For instance, by leveraging the scattered field data collected in an external observation domain, EM‐IS techniques offer a powerful approach for detecting cracks, voids, and other inhomogeneities in complex structures and composite materials.
This tutorial begins by reviewing the technical and physical foundations, the underlying mathematical equations, and the primary challenges of IS in high‐frequency EM. It will then provide an in‐depth exploration of classical and recently‐introduced solution approaches and algorithms, highlighting their capabilities, limitations, and the perspectives of both approximate and 'exact' (i.e., full‐wave) reconstruction methods.
This tutorial is designed for students, researchers, and professionals who aim to (a) learn the fundamentals of EM‐IS, (b) explore state‐of‐the‐art algorithms and methodologies, including the leading‐edge and recent advances in EM‐IS techniques, (c) understand the diverse applications of EM‐IS in research and industry, and (d) envision on‐going and future trends in this field, including the potential extension of EM‐IS concepts and techniques to other inspection technologies, such as ultrasounds.
Portable NMR systems are used in a variety of applications. Nano-NMR systems use charged nitrogen vacancies (NV-) in diamonds to detect NMR in molecules, bacteria, or small insects. Our NMR system uses the mechanical detection of NMR technique using magnetometers with ~ 0.3 nT/√Hz sensitivity sufficient to detect ~ 1012 polarized 1H spins (at 30 C and 100 mT field) within 10 mm of surface of composite materials. Traditional NMRs use coils with outputs proportional to Larmour = gBb, where g is the gyromagnetic ratio and Bb is a biasing field. Thus, coils have small signal-to-noise ratios at low fields. MEMS magnetometers’ output is not proportional to frequency enabling them to detect small Larmour. The sensitivity of mechanical magnetometers is superior to coil and microstripline detectors in low fields. Our approach is like and does not require field gradients that are required in some mechanical NMRs. In contrast to, our approach uses high-frequency, high-Q trampoline force sensors and they can be used in arrays for spatial filtering, multiplexing, ensemble averaging, zero-power edge computation, and gradiometry.
Defects are usually accompanied by trapped moisture, “broken” chemical bonds, and impurities such as salt and chlorine. The miniature NMR sensor may be used to map the chemical signatures of defects that may lead to improved mitigation strategies.
In Structural Health Monitoring (SHM), measured data that correspond to an wide set of operational and damage conditions are rarely available or very expensive to obtain. A way of probabilistic framework for the classification, investigation and labelling of data is discussed as an online strategy for SHM to aid both damage detection and identification, while using a limited number of the most informative labelled data.
A way of including physics knowledge and grey box modelling will also be discussed. Another, potential solution considers that information might be transferred, in some sense, between systems. A population-based approach to SHM (PBSHM) looks to both model and transfer this missing information, by considering data collected from groups of similar structures. A framework will be proposed to model a population of systems, such that datasets are only available from a subset of members. The ideas will be presented in a variety of engineering systems from experimental (test-rig) members to bridges and operational wind farms.