Large-area inspection of defects in metal plates using multi-mode guided acoustic waves and sparse sensor networks.

Various types of defects can be induced during the manufacturing or operation of engineering structures. For effective detection and characterization of the defects in large engineering structures, this paper proposes a large-area inspection technique that combines multi-mode guided acoustic waves with sparse sensor networks. The basic sparse sensor network employed in this study is composed of one transmitter and three receivers, distributed in a square lattice on the test plates. Multi-mode guided waves were excited and acquired by means of commercial single-element sensors of the network. To experimentally demonstrate the proposed technique, four different types of defects were simulated in aluminum test plates, including aluminum tape-based material addition, drilled material loss, indented deformation, and thermal embrittlement. For the evaluation of defects, acoustic response of each defect was analyzed based on the combination of linear vs. nonlinear acoustic characteristics, dependence on the type of the guided acoustic mode, and the directionality of the acoustic response on the network. Results indicate that each of the four representative defects can be uniquely identified (classified) and quantified using the proposed technique.