Advanced acoustic emission methods for damage mechanisms monitoring in aerospace materials

This thesis develops advanced Acoustic Emission (AE) techniques for monitoring and characterizing damage in aerospace materials. It focuses on AlSi10Mg produced by Selective Laser Melting (SLM) and on Carbon Fiber Reinforced Plastic (CFRP) composites. These materials are crucial for aerospace due to their unique mechanical properties. AlSi10Mg offers high strength and fatigue resistance. It is used in critical aerospace parts. CFRP composites provide stiffness strength and corrosion resistance, making them suitable for demanding aerospace environments. Monitoring damage in these materials is essential to ensure safety and structural integrity over time. This research aims to improve damage monitoring by combining traditional AE methods with deep learning frameworks. Structural Health Monitoring (SHM) is vital in aerospace. It allows early detection of material degradation and reduces the risk of in-service damages. Traditional SHM methods can be limited in accuracy for complex materials such as AlSi10Mg and CFRP composites. This thesis introduces a robust approach that uses advanced methods to address these challenges. Tensile tests were conducted on AlSi10Mg specimens built in different orientations. AE signals were recorded to examine their mechanical behavior. Continuous Wavelet Transform (CWT) was used to analyze these signals. This allowed differentiation between elastic and plastic deformation. Convolutional Neural Networks (CNNs) were then used to classify AE signals. Several CNN architectures, including AlexNet and SqueezeNet, were tested to improve classification accuracy. A novel approach was also introduced. It combines a Fuzzy Artificial Bee Colony (FABC) algorithm with CNN and CWT-scalogram analysis. This method includes data augmentation to improve robustness and prevent overfitting. For CFRP composites, a Deep Autoencoder (DAE) framework was developed to automate damage mode characterization during mechanical testing. The DAE reduced the complexity of AE signals and extracted essential features. These features were clustered to identify damage modes like matrix cracking, delamination, and fiber breakage. By automating damage classification, the DAE enhances SHM by providing accurate real-time damage assessments. This thesis shows that combining traditional AE features with deep learning models improves damage source classification for aerospace materials. These methods make SHM systems more efficient and precise. They offer advanced solutions for monitoring and maintaining structural integrity in aerospace. The research contributes to safer and more reliable aerospace applications.