Additive Manufacturing (AM) or 3D printing has become a popular manufacturing technique that helps to save materials during production. Modern industries have started incorporating printed structural parts into their structures, including those with critical applications, like in aerospace and civil. Similarly for structures made from metals or fibre reinforced polymers there is a need for structural health monitoring of the 3D-printed structural parts. This requires the development of accurate and reliable methods for evaluating and monitoring the structural integrity of such components. The Electromechanical Impedance (EMI) method is frequently used to evaluate the health condition of lightweight structures based on the local structural response in the high-frequency range. This study investigates the usage of EMI that is based both on surface bonded and embedded sensors. As sensors, the piezoelectric discs were used for the measurements. The measurements were made in the 1 kHz to 100 kHz frequency range for the Resistance (R) data. During the study, the simulated damage was introduced, and the sensors' responses were compared to determine the influence of embedding on the damage detection performance.
Additive manufacturing (AM) techniques can be applied for the production of carbon fiber reinforced polymer (CFRP) elements. There is a possibility of embedding fiber Bragg grating (FBG) sensors into elements during the manufacturing process. The embedded FBG sensors can be applied for measurements of the element internal temperature and strain. The goal of the paper is to analyze the influence of sub-zero temperatures on the AM CFRP material durability. The measurements were performed on the samples with FBG sensors embedded into the composite during the manufacturing process. It allows to online monitoring of internal strain in the material during its exposition on sub-zero temperatures and mechanical loading. Additionally, the influence of embedded FBG sensors and temperature on the mechanical strength was determined using the tensile tensile test. It was observed that the influence of embedded FBG sensors on the samples structure is neglected. The samples microstructures were also analysed using a scanning electron microscope (SEM). For the purpose of determination of the embedded sensors influence, the achieved results were compared with the results for similar samples without fiber optics. It was observed that exposition of CFRP material on sub-zero temperatures influenced on the microstructure of composite and the mechanical strength of the analysed samples.
KEYWORDS: Principal component analysis, Cross validation, 3D printing, Polymers, Artificial neural networks, Transducers, Data fusion, Structural health monitoring, Deep learning, Additive manufacturing
Additive manufacturing (AM) technology has been used for the creation of complex parts in different industries. The addition of defect detection and load sensing capabilities to these products can highly increase their values. Recently, modern industries have started incorporating AM components into their structures, including those with critical applications like aerospace and civil constructions. This requires the development of accurate and reliable methods for evaluating and monitoring the structural integrity of such components. The Electromechanical Impedance (EMI) method is frequently used to evaluate the health condition of lightweight structures based on the local structural response in the high-frequency range. This study investigates the usage of machine learning (ML) for the health-condition assessment of 3D-printed M3-X plates using EMI conductance (G) and resistance (R) data fusion. Piezoelectric wafers (PZTs) bonded to the center of the plates were used for the measurements. Drilled holes were created and repaired in multiple plates, and several EMI measurements were taken for the healthy, damaged, and repaired states of each plate. After fusing the R and G EMI measurement using a wide frequency range (1 kHz to 5 MHz), principal component analysis (PCA) was employed for feature reduction before a deep-learning approach was applied for diagnosis and damage classification. The findings demonstrate that the EMI method can be applied for the health assessment of AM polymers and is capable of differentiating between their healthy, damaged, and repaired states.
Additive manufacturing (AM) techniques can be applied for the production of carbon fiber reinforced polymer (CFRP) elements with embedded fiber Bragg grating (FBG) sensors. The goal of the paper is to analyze the influence of elevated temperature on the AM CFRP samples with FBG sensors embedded into and attached to the surfaces. It allows comparing the results in relation to the locations of the sensors. The samples structures will be analyzed using an optical microscope. Their tensile strength will be determined using the tensile test. The achieved results will be compared with the results for similar samples without fiber optics.
Additive manufacturing (AM) techniques can be applied for constructing three-dimensional (3D) objects with embedded fiber Bragg grating (FBG) sensors. The goal of the paper is to compare the influence of FBG sensors embedded into AM polymeric samples on structure durability. The samples will be manufactured from two polymers with different material properties. The analyzes will be focused on strain determined by FBG sensors due to thermal loading. Additionally, the tensile strength values of the polymeric samples will be determined. The experimental investigation will be performed for the samples, without /with sensors, after manufacturing and after temperature treatment.
Additive manufacturing (AM) is a name for techniques applied for constructing three-dimensional (3D) objects in a layer-by-layer process. Such methods can be applied for a variety of materials, like ceramics, polymers, and metals. It results in wide applicability of AM elements in many industrial branches, e.g. energetic, medicine or aerospace. Fiber Bragg grating (FBG) sensors due to their small dimensions and high chemical durability can be embedded into different material types. The goal of the paper is to analyze an influence of FBG sensors embedded into an AM polymeric samples on the polymeric structure durability. The samples will be manufactured using multi-jet printing method. The method was chosen due to its high accuracy of printed polymeric elements and the possibility of manufacturing products with complex shapes. The analyzes will be related to tensile strength determination of the samples with embedded FBG sensors. The comparisons will be performed for the samples without sensors and with sensors after manufacturing and after temperature treatment.
Additive manufacturing (AM) is a common name for a group of techniques that are applied for constructing three-dimensional (3D) objects in a layer-by-layer process. One of the AM methods is multi-jet printing, which offers high accuracy of printed polymeric elements and the possibility of manufacturing products with complex shapes. Elements manufactured using AM are exploited in many industrial branches, e.g. energetic, medicine or aerospace. It results in development of structural health monitoring (SHM) systems. Among a variety of sensors applied for SHM systems fibre Bragg grating sensors (FBG) are a very promising solution. Their advantages, such as small size, multiplexing capabilities and no calibration requirements, allow them to be embedded into different material types. The goal of the paper is to analyze possibility of FBG sensors embedding into an AM polymeric material. The analyzes will be related to the influence of the manufacturing process on FBG sensors as well as influence of temperature on the sensors after finished embedding process.
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