Metal additive manufacturing (AM) has experienced an explosive growth in interest within the aerospace and space sectors, but the adoption in real application has lagged interest. Process qualification (or lack thereof) is the primary reason and to date in situ sensing has failed to make a substantial impact, despite obvious utility. The primary premise of this talk is that current sensing techniques used in industry lag significantly behind what is possible and already implemented in other industries, and proper focus and division of labor between academia and industry can remedy the situation. Through this discourse we will assess whether or not current sensors used in AM are sufficient, what roadblocks may exist for academia to assist in developing solutions for industry, and how the in situ sensing community should focus their efforts for maximum impact.

Structural Health Monitoring (SHM) is a technique that involves employing damage detection techniques to assess critical civil infrastructure elements like bridges, wind turbines, buildings, and tunnels. It typically utilizes non-destructive methods and sensors embedded in or attached to structures for data collection and expert evaluation.These findings were developed to better help understand the relation between bridge stiffness and its fundamental frequencies.

This research deals with the determination of the anisotropic mechanical properties in the injection molded fiber reinforced polymer composite parts, evaluated using the fiber orientation distribution obtained with the help of xCT technique. The variation in the mechanical properties observed at different locations due to varying fiber orientation is also comprehensively studied for a variety of fibers.

Stress corrosion cracking (SCC) has been shown to cause catastrophic failure of structures. In the case of spent nuclear canisters, radioactive materials may leak through the cracks if they penetrate the tank wall. This work explores the adoption of ultrasonic waves for the purpose of crack detection in thick stainless-steel plates, first by Piezoelectric Transducer (PZT) and then pulsed laser (PL) actuation for a fully non-contact system.

Measuring the single-crystal elastic constants of polycrystalline materials has important engineering applications. This information is critical for predicting the macroscopic mechanical behaviour of materials and designing new materials with tailored mechanical properties. A new method for measuring the single-crystal elastic stiffness matrix of polycrystalline materials is presented. It builds on the capabilities of SRAS, a laser ultrasound technique for measuring the surface acoustic wave (SAW) velocity of a material. Combining measurements from multiple acoustic propagation directions with the elastic constants from literature, it is possible to determine the grains’ orientation. This paper details recent work for measuring the single-crystal elastic constants of polycrystalline materials combining SRAS with an inverse solver to extract both the orientation and elasticity from the SAW measurements.

Spatially Resolved Acoustic Spectroscopy has established itself as a powerful material characterisation technique capable of imaging the microstructure of a number of engineering alloys and semiconductor materials. The technique non-destructively utilises laser ultrasonics to robustly, rapidly, and repeatably measure controlled surface acoustic wave velocities – these can be mapped to image material grain contrasts (SRAS).

Most modern railways use continuous welded rail (CWR) because they support higher transport speeds, provide less friction, and generally require less maintenance. However, thermal buckling of CWR has been a long-standing challenge for the railroad industry. Rail neutral temperature (RNT) is the temperature at which the longitudinal stress of a rail is zero. Due to the lack of expansion joints, CWR develops internal tensile or compressive stresses when the rail temperature is below or above, respectively, the RNT. Therefore, thermal stress or RNT measurement and management of CWR become more important for railroad maintenance. In this work, the team proposes a practical and nondestructive method for RNT estimation exploiting local resonances in rails.

Synthetic aperture radar (SAR) imaging is one of the radar NDE techniques used for remote sensing of civil infrastructures. SAR imaging has been used for remote detection of moisture content, chloride content, and steel rebar corrosion in concrete specimens. Parameters extracted from SAR images are used for condition assessment, including integrated SAR amplitude (Iint), average maximum SAR amplitude (Iavg), and critical contour area (Ac). The objective of this paper is to investigate the effects of moisture and chloride content on the interdependency of critical contour area with integrated SAR amplitude and average maximum SAR amplitude. Laboratory portland cement concrete panel specimens of different water-to-cement (w/c) ratios (0.4, 0.5, and 0.55) were manufactured and scanned inside an electromagnetic anechoic chamber during the drying process of the specimens. From our result, it was found that the interdependency of critical contour area with integrated SAR amplitude and average maximum SAR amplitude during moisture variation is different from the one during chloride variation for different w/c ratios.

This paper presents a novel method for monitoring continuous welded rails (CWR) to estimate longitudinal stress and determine the rail neutral temperature (RNT). The technique combines vibration measurements, finite element analysis (FEA), and machine learning (ML). FEA establishes the relationship between boundary conditions and stress, serving as the foundation for training an ML algorithm using field data from accelerometers on the track. In the field tests, the method accurately predicted RNT and stress levels, with the ML model demonstrating the ability to learn effectively from experimental data. This approach holds promise for improving rail safety, maintenance, and performance optimization.