Inventor : Dr Matteo Seita, Granta Design Assistant Professor in Engineering & XtaLight
The performance of metal parts depends on the material’s microscopic structure, which thus must be assessed routinely for quality control of engineering components. Existing characterisation techniques are either slow, quantitative and expensive, or qualitative and fast. The inventor has developed a new method which combines optical microscopy with machine learning models to carry out quantitative, fast and low-cost assessments of individual metallic components.
This new technique is called “Directional Reflectance Microscopy” (DRM) and enables direct imaging of the crystalline nature of metals in a similar fashion to conventional electron and x-ray diffraction techniques. In contrast to those, however, DRM does not require the material to be placed in a vacuum or confined in a shielded environment, offering greater flexibility on the type of material and sample size to be analysed. Because of these features, and because of the reduced cost of optical technology, DRM enables multiple, life-sized components to be assessed directly after production, which is a paradigm shift for quality control of metal parts in industry.
The first market for the technique is the quality control of metal parts in industries such as aerospace. The challenge for the i-Team will be to investigate and assess the possible route to market for the technique, by identifying and interviewing relevant industry experts from a wide range of sectors and companies. This will include looking at the market needs for this type of analysis for other types of materials (eg ceramics and composite materials).