The field of material characterization and inspection is rapidly evolving, with a focus on developing innovative methods for non-invasive and automated assessment of material properties. Recent developments have centered around the use of artificial intelligence, computer vision, and robotics to improve the accuracy and efficiency of material inspection. Notably, researchers are exploring the application of these technologies to a wide range of fields, including healthcare, construction, and environmental monitoring.

One of the key trends in this area is the development of methods for inferring subsurface physical properties from surface measurements. This has significant implications for fields such as healthcare, where non-invasive assessment of tissue properties could revolutionize disease diagnosis and monitoring.

Another area of focus is the development of automated systems for material inspection, including the use of robotics and computer vision to analyze material properties in real-time. These systems have the potential to greatly improve the efficiency and accuracy of quality control processes in industries such as construction.

Some noteworthy papers in this area include:

• A study on Visual Surface Wave Elastography, which proposes a method for inferring subsurface physical properties from surface wave measurements.
• A paper on SlumpGuard, an AI-powered system for automated concrete slump prediction via video analysis, which has the potential to improve the efficiency and accuracy of quality control in construction.
• Research on Force-Based Viscosity and Elasticity Measurements for Material Biomechanical Characterisation with a Collaborative Robotic Arm, which demonstrates the potential of robotic systems for precise estimation of tissue biomechanical properties.
• A study on Detección y Cuantificación de Erosión Fluvial con Visión Artificial, which proposes an artificial intelligence-based approach for automatic identification of eroded zones and estimation of their area.
• A paper on the Design and Development of an Automated Contact Angle Tester (ACAT) for Surface Wettability Measurement, which presents a fully integrated robotic work cell for automating the measurement of surface wettability on 3D-printed materials.