CorrosionIQ

Anticipate corrosion risks using Artificial Intelligence and digital twins, leveraging historical test data to pinpoint critical areas, guide physical testing, and accelerate design and validation cycles.

Corrosion Risk Prediction via Digital Twins and AI

Physical corrosion testing often requires costly prototypes and extended testing cycles, increasing time-to-market and development costs during design and validation.

CorrosionIQ utilizes predictive models and digital twins to forecast corrosion risks, enabling engineering teams to focus physical testing precisely where it is needed most.

From Historical Test Data to Predictive Insights

The solution integrates 3D CAD models of each component with historical documentation, imagery, and past test results to identify underlying patterns related to corrosion behavior.

Results are mapped directly onto the 3D model via an intuitive visual risk map, enabling teams to:

Identify high-probability corrosion zones.
Pinpoint design flaws or areas requiring engineering revision.
Reduce prototype counts and focus physical testing on targeted high-risk zones.
Dramatically shorten validation timelines and lower costs.
Capitalize on historical testing datasets for future engineering evaluations.

Implementation can begin with a Proof of Concept on a target component and scale progressively across additional parts.

CorrosionIQ embeds predictive capabilities into your engineering processes to streamline physical testing and mitigate structural risks early.