RESEARCH & DEVELOPMENT TO DATE

XRD-SIIM was initially developed as part of an engineering doctorate at the University of Sheffield Advanced Manufacturing Research Centre as a method for detecting machining-induced white layers in aerospace applications. Since that time, the technique has been in development at AMRC for detection of a broader range of damage types, with efforts focused on validation of the method against the incumbent microscopy process with upwards of 98% classification agreement in terms of surface integrity passes/failures and defect layer sizing to within 2 µm achieved. More recently, work has focused on proof-of-concept for on-machine inspection, as well as inspection of real engineering components with complex geometry, such as a nickel-based superalloy turbine disc and a titanium orthopaedic implant. Measurement systems analysis showed that XRD-SIIM offers benefits over the incumbent microscopy process, by removing operator interpretation factors.

Patent

The University of Sheffield has a patent granted for XRD-SIIM covering Non-destructive detection of surface and near surface abnormalities in a metallic product. The patent has been granted in various regions
USA - US11815477B2
Canada - CA3135684A1
Singapore - SG11202111005VA
Europe  - EP3963320 validated in the UK, France, Italy, Spain, Sweden, Ireland, Poland, Belgium, Switzerland, Luxembourg, Monaco, Liechtenstein, Turkey.
Germany - DE602020040270T2

ONGOING DEVELOPMENT

Our ongoing developments are focused on the following:

• Development of integrated on-machine XRD-SIIM hardware designed around machined surface inspection.

• Continuing to expand the capability of XRD-SIIM in terms of material and component geometries via case studies with industry.

Future developments target:

• Translation of the XRD-SIIM system into industry.

• Releasing the XRD-SIIM analysis software for use outside of the AMRC. 

• Implementation of international standards for XRD-SIIM.

• Development and demonstration of feedback control of a machining operation based on defect condition assessed on-machine