Quantitative damage assessment using guided ultrasonic waves signals

摘要：

Inverse algorithms based on neural network were developed for quantitative assessment of damage in structures, e.g. cracks in aluminum plates and delamination in laminate composite beams or plates. It uses a concept of digital damage fingerprints (DDFs) extracted from scattered wave signals, which are the input for training of artificial neural network. The trained neural network was used for inverse assessment of the damage, and the algorithm was validated by experiments with actual damage introduced in aluminum plates and laminate composite beams or plates, where DDFs were extracted from networks of piezoelectric elements (PZT) attached to the surface of the structures for activating and capturing of guided ultrasonic wave signals. The results predicted by the algorithm show good accuracy in defining damage parameters, such as central position, size, orientation etc., for cracks and delamination in the structures.

关键词： Damage assessment Guided aave signals Simulations Damage signatures Artificial intelligence

作者: Lin Ye Zhongqing Su Ye Lu

作者单位: School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney,NSW2006, Austr Department of Mecahnical Engineering, Hong Kong Polytechnic University, Hung Horn, Kowloon, Hong Kon Department of Civil Engineering, Monash University, VIC 3800, Australia

会议类型: 国际会议

会议名称: 2012 International Symposium on Structural Integrity 2012国际结构完整性学术研讨会 ISSI 2012

会议地点: 济南

会议语种:英文

页码: 35-42

在线出版日期: 2012-10-31（万方平台首次上网日期，不代表论文的发表时间）