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.
Lightweight complex-shaped parts are imposing themselves as inevitable in modern industry. This has induced the improvement of additive manufacturing (AM) processes and, hence, their transformation from the prototyping state into real industrial production. Such a transformation necessitates the establishment of reliable structural health monitoring (SHM) techniques for AM structures, to ensure their safe use and extend their lifetime. Research contributions over the last few decades have shown a significant potential of ultrasonic Lamb waves (LWs) for SHM of both metallic and composite structures, thanks to their favorable propagation characteristics and sensitivity to various types of structural damage. The current work investigates the propagation characteristics of LWs and examines their potential for damage imaging and localization in AM structures. To this end, pristine and damaged plates were manufactured using different materials and printing techniques/layouts. LWs of a range of typical central frequencies (50, 100, and 150 kHz) were excited at the surface of the plates using PZT and MFC transducers. Area scans were performed, using a scanning laser vibrometer, to receive the propagating waves. The influence of printing patterns on the propagation velocities of the fundamental LW modes was scrutinized, as compared to the theoretical velocities in the printing materials, assumed uniform and isotropic. Further, various damage imaging techniques were explored to detect and localize damage in the AM plates. The obtained results are considered an important step towards the application of LW-based techniques for SHM of additively manufactured structures.
Investigated is the ability of ultrasonic guided waves to detect flaws and assess the quality of friction stir welds (FSW). AZ31B magnesium plates were friction stir welded. While process parameters of spindle speed and tool feed were fixed, shoulder penetration depth was varied resulting in welds of varying quality.
Ultrasonic waves were excited at different frequencies using piezoelectric wafers and the fundamental symmetric (S0) mode was selected to detect the flaws resulting from the welding process. The front of the first transmitted wave signal was used to capture the S0 mode. A damage index (DI) measure was defined based on the amplitude attenuation after wave interaction with the welded zone. Computed Tomography (CT) scanning was employed as a nondestructive testing (NDT) technique to assess the actual weld quality. Derived DI values were plotted against CT-derived flaw volume resulting in a perfectly linear fit. The proposed approach showed high sensitivity of the S0 mode to internal flaws within the weld. As such, this methodology bears great potential as a future predictive method for the evaluation of FSW weld quality.
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