WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning
arXiv:2605.20311v1 Announce Type: new Abstract: Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. However, inferring the spatial location of defects from pitch-catch measurements remains weakly constrained when only a limited set of damage locations is available for training. As a result, models trained to predict defect locations may perform well on seen cases but generalize poorly to unseen regions of the structure. This paper proposes WaveGraphNet, a coupled inverse--forward graph learning fram
The increasing sophistication of AI, particularly in graph learning and inverse problem solving, allows for significant advancements in structural integrity monitoring.
This development represents a leap forward in non-destructive testing, enabling more precise and reliable defect detection in critical infrastructure and advanced materials like composites.
Traditional, less constrained methods for damage localization are being superseded by physics-consistent, AI-driven approaches that improve accuracy and generalization.
- · Aerospace Industry
- · Civil Engineering
- · AI/ML Researchers
- · Manufacturers of Composite Materials
- · Traditional NDT Methods (less accurate)
- · Maintenance Firms reliant on manual inspections
More reliable detection of material defects, extending product lifecycles and enhancing safety.
Reduced maintenance costs and downtime for complex structures and vehicles.
Accelerated design and deployment of new advanced materials, knowing structural integrity can be precisely monitored.
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Read at arXiv cs.LG