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Advanced acoustic emission methods for damage mechanisms monitoring in aerospace materials
Get PDFThis 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
The determination of stress distribution and elastic proprties for heterogeneous materials with hybrid finite elements
No full textStructural transitions in a NiTi alloy: a multistage loading-unload cycle
No full textNiTi shape memory alloys (SMAs) are increasingly used in many engineering and medical applications, because they combine special functional properties, such as shape memory effect and pseudo-elasticity, with good mechanical strength and biocompatibility. However, the microstructural changes associated with these functional properties are not yet completely known. In this work a NiTi pseudo-elastic alloy was investigated by means of X-ray diffraction in order to assess micro-structural transformations under mechanical uniaxial deformation. The structure after complete shape recovery have been compared with initial state
Cyclic microstructural transitions and fracture micromechanisms in a near equiatomic NiTi alloy
No full textIn this work stress-induced microstructural transitions and crack initiation and growth mechanisms in a near equiatomic NiTi shape memory alloy have been analyzed, by means of both X-ray diffraction (XRD) and scanning electron microscopy (SEM) investigations. In particular, miniaturized dog-bone shaped specimens and a special testing machine have been used, which allow in situ XRD and SEM investigations during mechanical loading. Direct and reverse stress-induced phase transition mechanisms, between the parent austenitic phase and the product martensitic one, have been captured by XRD while crack initiation and propagation mechanisms have been observed by means of SEM investigations. These analyses revealed that stress-induced transformations occurs near the crack tip, as a consequence of the highly localized stress, which significantly affect the crack propagation mechanisms with respect to common metallic alloys. In fact, blunting does not occurs during mechanical loading and, in addition, complete crack closure is observed during unloading, as a consequence of the reverse transformation from product to parent phase. © 2013 Elsevier Ltd. All rights reserved
Functional and Structural Fatigue of a Pseudoelastic NiTi Alloy within the Stress-Induced Transformation Regime
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Fatigue properties of a pseudoelastic NiTi alloy: Strain ratcheting and hysteresis under cyclic tensile loading
Get PDFFatigue of pseudoelastic NiTi within the stress-induced transformation regime: A modified Coffin-Manson approach
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