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Imaging for Condition Monitoring 

The Problem: Traditional reliability models require multi-year physical tests to certify a single compound which can be elastomers, epoxies, thermoplastics, and thermosets.

K-Sense Physics-based AI architectures understand the underlying chemistry of oxidation and cross-linking.
 

• 60% Reduction in Testing Duration 

• Physics-Driven Extrapolation 

• High Confidence Ratio by Tracing Compound Specific Kinetics

 

 

K-Sense Superiority to Statistical Models

We validate our digital twins using a rigorous two-step protocol that ensures your field data matches our lab-trained models.

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Step 1: Limited Training Set required

We ingest data from coupon-level accelerated tests (Thermal, Radiation, or Synergistic). This "seeds" the PINN with the material’s specific degradation kinetics.

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Step 2: High Accuracy in Long-Term Predictions

We compare K-Sense predictions against independent "Long-Tests"—real-time service data or extended duration lab tests. The Result: Our models consistently show high agreement with long-term mechanical decay (Elongation at Break, Tensile Strength, and Modulus) that legacy Arrhenius models simply cannot capture.

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