Prof. Fucai Li
Project Description and Objectives
Many large-scale structures are made of metallic or composite tubular structures with joints, junctions, stiffeners or weld lines. If damage such as corrosion and fatigue cracking, which may be induced by manufacturing faults, improper use, wear, fatigue, impact, sabotage, etc., is not detected on time it can lower the integrity of critical sections of the tubular structures significantly. Potentially leading to the catastrophic failure of the entire structure, with disastrous consequences.
Over the past decades, there has been a substantial advancement in materials science and signal processing/pattern recognition techniques, e.g. advanced sensors and sensor network, guided ultrasonic waves, artificial intelligence. This is associated with the exponential development of informatics, computing and communication technologies. These developments have paved technical paths and presented a unique opportunity to address the fundamental issues and break through certain technical barriers in developing a guided wave-based damage identification algorithm for large-scale structures.
Work of the guided wave-based structural health monitoring project consists of:
Studying the basic knowledge of PZT actuators and sensors, guided waves and signal processing;
Experiments on how PZT actuators can excite guided waves in metallic structures and capture guided wave signals by using PZT sensors;
Processing the guided wave signals captured from intact and damaged structures and studying how to identify the damage in different kinds of structures, such as plates and tubes.
Students majoring in mechanical engineering.
Finish a series of experiments and data processing work.
Make a presentation about the research.