Sound Mathematics Ltd (SML) is pioneering a novel approach to non-destructive testing (NDT) and structural health monitoring (SHM) data processing using Explainable AI - a software application for interpreting images of damage (or any other images with ill-defined features) based on customised decision trees.
We are looking for co-development partners / early adopters who:
- Have existing PAUT, eddy current or radiography data we can use to create images of damage or maybe, images they made themselves and are willing to share with us (no large datasets are required),
- Are prepared to dedicate a bit of time to discuss their data interpretation with us (i.e. perform informal labelling),
- Are interested in shaping the future of NDT and SHM data analysis - maybe, by using our decision trees to create context for their nural networks!
In return, you'll gain early access to our software, influence its development, and benefit from a solution tailored to your specific needs.
If you're intrigued by the potential of Explainable AI to revolutionize your NDT or SHM workflows, let's t
