Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an...

Area of Science:

• Materials Science
• Mechanical Engineering
• Non-Destructive Testing

Background:

• Guided wave monitoring is crucial for structural health assessment.
• Accurate defect detection in complex structures like aluminum joints remains challenging.
• Existing methods often struggle with uncertainties and computational demands.

Purpose of the Study:

• To develop and validate a robust defect detection and identification scheme for aluminum joints.
• To enhance the computational efficiency of guided wave-based damage identification.
• To investigate the influence of sensor placement on identification accuracy.

Main Methods:

• Guided wave testing was employed, focusing on scattering coefficients as a damage feature.
• A Bayesian framework was developed to handle modeling and experimental uncertainties.
• A hybrid wave and finite element (WFE) approach, coupled with a kriging surrogate model, was used for efficient defect size prediction.

Main Results:

• The proposed Bayesian framework successfully identified defects in aluminum joints.
• The hybrid WFE and kriging surrogate model significantly improved computational efficiency.
• Numerical and experimental studies validated the effectiveness of the damage identification scheme.

Conclusions:

• The developed approach provides a reliable and computationally efficient method for defect detection in aluminum joints.
• The framework's ability to account for uncertainties enhances its practical applicability.
• Sensor location is a critical factor influencing the accuracy of damage identification.