Generation, Annihilation and Flow of Structural Information in Ultrasonic Nondestructive Evaluation

Non-destructive testing using ultrasound is based on the interaction of sound waves with the object being tested and any defects it may contain. The aim is to extract as much information as possible about the object and its defects from the scattered wave field. In this paper, the concept of information in the context of ultrasonic testing is formalized and quantified physically for the first time. To this end, a balance equation for information is derived, analogous to Poynting's theorem for elastic energy. Various examples demonstrate how structural information is generated and annihilated within a component and along which pathways it travels from the defect to the sensor. Subsequently, the significance and potential of this new information concept for practical ultrasonic testing, structural health monitoring, numerical simulation, and machine learning are discussed. Finally, similarities and differences to mathematical Shannon information and statistical Fisher information are highlighted.

A critical note on the sideband peak count-index technique: failure for nonlinear damage characterization of impacted CFRP plates

It is widely accepted, that nonlinear elastodynamic methods are superior to linear methods in detecting early stages of material deterioration. A number of recently developed methods are reported to be particularly sensitive to nonlinearities and thus appropriate to indicate early damage. We applied systematically one of the methods, the sideband peak count index (SPC-I), to a series of increasingly damaged carbon fiber reinforced plastic (CFRP) plates. Our data leads to different conclusions. The SPC-I values are influenced by (usually undocumented) variations in the index calculation procedure, which is not acceptable for a robust method. Moreover, the behavior of the index when the ultrasound amplitude is varied contradicts material nonlinearity as a direct and significant contributor to the index value. To clarify the apparent contradiction of our results with the previously published statements, it is recommended that (a) our data are re-evaluated by independent researchers and (b) the experiments already published are repeated or (if sufficient data is available) also re-evaluated.