Estimating cutter-tooth abrasion where visual inspection is impossible using PINNs to solve inverse problems from motor signals.
Read Time: 8 min read | Published: 2025-01-15
In dredging, visual inspection of cutter heads is impossible during operation. Wear progression directly correlates with a 12-18% increase in energy consumption per 100 operating hours (Mechanical Sciences, 2025). As teeth wear, the cutting force to normal force ratio drops dramatically from 33:1 to 5:1, yet current practice still relies on inefficient manual inspection stops.
We utilize a dual-network inverse PINN architecture (arXiv:2512.12074) where both the solution (cutting behavior) and the unknown parameter (tooth sharpness) are learned simultaneously from the same loss function. This physics-based approach uses the established relationship between tooth geometry and mechanical resistance to infer wear directly from motor torque and pump pressure.
While direct validation in turbid water is challenging, physics-guided deep learning models in analogous high-speed milling applications have achieved ~3.9% MAPE in wear estimation. By incorporating the specific 33:1 to 5:1 force ratio change as a physical constraint, our model can mechanistically distinguish between harder soil strata and worn teeth.
Proceed with a gated pilot program. Do not rely on this as the sole indicator for maintenance until a wider library of soil-type training data is established.
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