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Detection of Crack Initiation and Growth Using Fiber Bragg Grating Sensors Embedded into Metal Structures through Ultrasonic Additive ManufacturingStructural health monitoring (SHM) is a rapidly growing field focused on detecting damage in complex systems before catastrophic failure occurs. Advanced sensor technologies are necessary to fully harness SHM in applications involving harsh or remote environments, life-critical systems, mass-production vehicles, robotic systems, and others. Fiber Bragg Grating (FBG) sensors are attractive for in-situ health monitoring due to their resistance to electromagnetic noise, ability to be multiplexed, and accurate real-time operation. Ultrasonic additive manufacturing (UAM) has been demonstrated for solid-state fabrication of 3D structures with embedded FBG sensors. In this paper, UAM-embedded FBG sensors are investigated with a focus on SHM applications. FBG sensors embedded in an aluminum matrix 3 mm from the initiation site are shown to resolve a minimum crack length of 0.286 ± 0.033 mm and track crack growth until near failure. Accurate crack detection is also demonstrated from FBGs placed 6 mm and 9 mm from the crack initiation site. Regular acrylate-coated FBG sensors are shown to repeatably work at temperatures up to 300 ∘ C once embedded with the UAM process.
Steel, which has high tension and compression strength, is a widely used civil engineering material in constructing building, bridge, pipelines, and other structures. However, steel has a well-known weakness, which is suspected to corrosion. Steel corrosion would significantly impact the reliability and safety of steel structures. Accurately locating and assessing the corrosion of steel structures would contribute to timely maintenance and thus, extend the service life of the steel structures. Although advances have been made to use nondestructive evaluation (NDE) technologies to locate and assess corrosion on steel structures, due to the lack of labor and budget for frequent NDE assessment on steel structures, remote and real-time approaches to locate and assess corrosion are still in great needs. Fiber optic sensors, especially, fiber Bragg gating (FBG) sensors, with unique advantages of real-time sensing, compactness, immune to EMI and moisture, capability of quasi-distributed sensing, and long life cycle, will be a perfect candidate for long-term corrosion assessment. However, due to the fact that FBG is a localized sensor, it is very challenging to locate corrosion using FBG sensors. In this study, algorithms are developed to locate corrosion on steel structures using FBG sensors. Detail sensing principle, localization algorithm development and calibrationmore »
Performance assessment of prestressed concrete bridge girders using fiber optic sensors and artificial neural networksStructural health monitoring (SHM) activities are essential for achieving a realistic characterisation of bridge structural performance levels throughout the service life. These activities can help detect structural damage before the potential occurrence of component- or system-level structural failures. In addition to their application at discrete times, SHM systems can also be installed to provide long-term accurate and reliable data continuously throughout the entire service life of a bridge. Owing to their superior accuracy and long-term durability compared to traditional strain gages, fiber optic sensors are ideal in extracting accurate real-time strain and temperature data of bridge components. This paper presents a statistical damage detection and localisation approach to evaluate the performance of prestressed concrete bridge girders using fiber Bragg grating sensors. The presented approach employs Artificial Neural Networks to establish a relationship between the strain profiles recorded at different sensor locations across the investigated girder. The approach is capable of detecting and localising the presence of damage at the sensor location without requiring detailed loading information; accordingly, it can be suitable for long-term monitoring activities under normal traffic loads. Experimental laboratory data obtained from the structural testing of a large-scale prestressed concrete bridge girder is used to illustrate the approach.
HIGH TEMPERATURE CHARACTERIZATION OF FIBER BRAGG GRATING SENSORS EMBEDDED INTO METALLIC STRUCTURES THROUGH ULTRASONIC ADDITIVE MANUFACTURINGEmbedded fiber Bragg grating (FBG) sensors are attractive for in-situ structural monitoring, especially in fiber reinforced composites. Their implementation in metallic structures is hindered by the thermal limit of the protective coating, typically a polymer material. The purpose of this study is to demonstrate the embedding of FBG sensors into metals with the ultimate objective of using FBG sensors for structural health monitoring of metallic structures. To that end, ultrasonic additive manufacturing (UAM) is utilized. UAM is a solid-state manufacturing process based on ultrasonic metal welding that allows for layered addition of metallic foils without melting. Embedding FBGs through UAM is shown to result in total cross-sectional encapsulation of the sensors within the metal matrix, which encourages uniform strain transfer. Since the UAM process takes place at essentially room temperature, the industry standard acrylate protective coating can be used rather than requiring a new coating applied to the FBGs prior to embedment. Measurements presented in this paper show that UAM-embedded FBG sensors accurately track strain at temperatures higher than 400 C. The data reveals the conditions under which detrimental wavelength hopping takes place due to non-uniformity of the load transferred to the FBG. Further, optical cross-sectioning of the test specimensmore »
Smooth camber morphing aircraft offer increased control authority and improved aerodynamic efficiency. Smart material actuators have become a popular driving force for shape changes, capable of adhering to weight and size constraints and allowing for simplicity in mechanical design. As a step towards creating uncrewed aerial vehicles (UAVs) capable of autonomously responding to flow conditions, this work examines a multifunctional morphing airfoil’s ability to follow commands in various flows. We integrated an airfoil with a morphing trailing edge consisting of an antagonistic pair of macro fiber composites (MFCs), serving as both skin and actuator, and internal piezoelectric flex sensors to form a closed loop composite system. Closed loop feedback control is necessary to accurately follow deflection commands due to the hysteretic behavior of MFCs. Here we used a deep reinforcement learning algorithm, Proximal Policy Optimization, to control the morphing airfoil. Two neural controllers were trained in a simulation developed through time series modeling on long short-term memory recurrent neural networks. The learned controllers were then tested on the composite wing using two state inference methods in still air and in a wind tunnel at various flow speeds. We compared the performance of our neural controllers to one using traditional position-derivativemore »