Structural health monitoring (SHM) is a technology of employing damage detection strategies to monitor the integrity of a structure in real time using a network of integrated sensors with advanced data acquisition, computation, and communication approaches. Unlike many commonly known localized nondestructive approaches, such as ultrasonic, acoustic emission, eddy current, thermal field method ah, Lamb waves, or guided elastic waves, have the advantage of wave propagation over large distances with little loss of amplitude. Thus, a significant advantage is that the sensors do not need to be located in the vicinity of the damage.
This research focuses on improving Lamb wave damage detection and diagnostic strategies using piezoelectric-based actuators. For example, a new beamforming algorithm using MacroFiber Composite (MFC) actuators has been developed to accurately predict the beamforming/beamsensing direction as well as decresing the main lobe width and side lobe magnitudes. We have also developed a robust damage classification system using a machine learning method that is able to classify various types of damage in metallic structures.
Some example publications:
Kim, D., Philen, M., "Guided Wave Beamsteering using MFC Phased Arrays for Structural Health Monitoring: Analysis and Experiment," Journal of Intelligent Material Systems and Structures, Vol. 21, pp. 1011-1024, 2010.
Kim, D., Philen, M., "Review of Damage Evaluation in Guided Lamb Wave Structural Health Monitoring," Journal of Intelligent Material Systems and Structures, Accepted for publication.
Kim, D., Philen, M., “Damage Classification using Adaboost Machine Learning for Structural Health Monitoring,” Proceedings of SPIE - The International Society for Optical Engineering: Smart Structures/NDE, March 6-10, San Diego, CA, 2011.
Kim, D., Philen, M., “Beamsteering of MFC Phased Arrays for Structural Health Monitoring: Analytical and Experimental Investigations,” 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, May 4-7, 2009.