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.
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.
The electromechanical impedance (EMI) method is a high frequency based local structural damage detection technique. The recent advances in sensor network study have led to study of direct-coupled mechanical impedance (DCMI) signals based damage indices using a modified probability weight function for damage imaging in the structure. Further, a novice fused data which combines the information of sensor resistance (R) and conductance (G) is also studied in robust damage detection in structural health monitoring (SHM) due to its capabilities of extracting multiple information. RMSD is the popular damage metrics in describing the behaviour of signal in damage quantification in the frequency domain based EMI method. This work implements a modified probability weight function using the optimal radius of sensing region for the large and small steps/intervals of radius. Further, a comparative damage imaging study done among F, DCMI and filtered detrend (FD) data based RMSD damage index. The proposed methodology is implemented for the glass fiber reinforced polymer (GFRP) composite material with impact damage.
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.
The recent advances in sensor technologies have led to the daunting task of combining the information and robust decision making for damage detection in structural health monitoring (SHM) due to its capabilities of extracting multiple information. The Electromechanical Impedance (EMI) method employs high frequencies range in assessing the local structural response based on structural health monitoring (SHM). This work describes the quantification of the frequency domain on the Al plate using principal component analysis (PCA) based hotteling’s T2 damage curve in describing the behaviour of signal. PCA used to reduce multivariable complex data set to lower dimension in order to reveal simplified statistical patterns. The EMI method used damage metrics as a tool to separate quantitatively or qualitative pre-process data of EMI spectra into classes depending on the damage presence, level and location. The information of sensor’s resistance (R) and conductance (G) is studied in the frequency domain and data fusion is realized at the variable level using fused variable F. The proposed methodology is tested and validated for Al material by creating drilled holes.
This paper describes the electromechanical impedance (EMI) method based structural health monitoring using sensor data fusion approach. The data fusion of different attributes is more effective over a single data in achieving reasonable accuracy and precision. The paper investigates an electromechanical impedance (EMI) method applied to the structural health monitoring of sensor network of thin composite plates using distributed sensor data fusion techniques and a single sensor for different levels of delamination. The information from multiple sensors and single sensor is studied in the frequency domain and a new optimized fused criteria of variable admittance (Y) and conductance (G) is explained by damage metric root mean square deviation (RMSD). The experiments are performed on a thin composite plate with attached piezoelectric transducers at different locations and a plate with single transducer with different delamination levels.
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