MaterialScan

MaterialScan is a term used in materials science and industrial inspection to describe integrated approaches for non-destructive analysis of a material's composition, structure, and properties by combining multiple imaging and sensing modalities with data analytics. The goal is to infer microstructural features and mechanical or functional properties without destructive sampling. Typical implementations fuse data from optical, X-ray, infrared, Raman or other spectroscopic measurements with ultrasonic or terahertz imaging to produce a coherent characterization map.

In practice, a MaterialScan workflow involves data acquisition from several modalities, co-registration to a common spatial frame, and computational fusion using statistical models, machine learning, or physics-based simulations. Outputs may include phase distribution, porosity, impurity concentration, layer thickness, defect maps, or estimated mechanical properties such as stiffness or hardness.

Applications include manufacturing quality control, failure analysis, material development, cultural heritage assessment, and recycling or sorting of materials in the circular economy.

Advantages include non-destructive analysis, rapid screening, high spatial resolution, and multimodal insight. Limitations involve dependency on calibration data, challenges in data integration, potential biases in models, the need for specialized equipment and expertise, and interpretation can be complex.

Related topics include non-destructive testing, hyperspectral imaging, machine learning for materials science, and computed tomography.