Machine Learning-Enhanced Ultrasonic Phased Array Inspection of Aerospace Composite

Jaafar Jasim Mahdi  Mohammed; Dhurgham Ahmed Hameed  Jasim; Humam Thamer Ibrahim  Hamdan; Karim Nazim Karim  Muhammad

doi:10.51699/cajmns.v7i1.3043

Jaafar Jasim Mahdi Mohammed Al-Nahrain University, College of Engineering, Department of Biomedical Engineering, Iraq
Dhurgham Ahmed Hameed Jasim Al-Nahrain University, College of Engineering, Department of Biomedical Engineering, Iraq
Humam Thamer Ibrahim Hamdan Al-Nahrain University, College of Engineering, Department of Biomedical Engineering, Iraq
Karim Nazim Karim Muhammad Central Technical University, Electrical Engineering Technical College, Department of Medical Device Engineering Technologies, Iraq

DOI: https://doi.org/10.51699/cajmns.v7i1.3043

Keywords: Ultrasonic Phased Array, Non-Destructive Testing, Composite Materials, Machine Learning, Convolutional Neural Networks, Aerospace Structures

Abstract

This study evaluates the effectiveness of advanced ultrasonic phased array imaging systems for non-destructive evaluation (NDE) of carbon fiber reinforced polymer (CFRP) composites in aerospace structures. A hybrid method combining time-of-flight diffraction (TOFD) with convolutional neural network (CNN) image processing was developed and validated, demonstrating a 94.7% defect detection accuracy for delamination, porosity, and impact damage compared to 78.3% for traditional methods, with a 40% reduction in false positives. Using a 64-element phased array transducer at 5 MHz and a multi-modal data acquisition system, significant improvements in signal-to-noise ratio (SNR) and lateral resolution were achieved, indicating the method's potential for in-service inspection of complex aircraft components.

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