NONLINEAR ACOUSTIC TECHNIQUE FOR MONITORING POROSITY IN ADDITIVELY MANUFACTURED PARTS

Ultrasonic wave based techniques are widely used for damage detection, and for quantitative and qualitative characterization of materials. In this study, ultrasonic waves are used for probing the response of additively manufactured 316L stainless steel samples as their porosity changes. The additively manufactured stainless steel specimens were fabricated using a laser powder bed fusion (LPBF) metal 3D printer. Four different levels of porosity were obtained by suitably controlling the LPBF process parameters. For generating ultrasonic waves, lead zirconate titanate (PZT) transducers were used. The signals were generated and propagated through the specimens in a transmission mode setup. Both linear and nonlinear analyses were used during the signal processing of the recorded signals for damage characterization. Linear ultrasonic parameters such as the time-of-flight (related to wave velocity) and signal amplitude (related to wave attenuation) were recorded. The nonlinear ultrasonic parameter, Sideband Peak Count - Index (SPC-I), was obtained by a newly developed nonlinear analysis technique called the SPC-I technique. Results obtained for the specimens were analyzed and compared for both linear and nonlinear ultrasonic analyses. Finally, the effectiveness of the SPC-I technique in monitoring porosity levels in additively manufactured specimens is discussed.

Gaussian process NARX model for damage detection in composite aircraft structures

This paper demonstrates the Gaussian process regression model's applicability combined with a nonlinear autoregressive exogenous (NARX) framework using experimental data measured with PZTs' patches bonded in a composite aeronautical structure for concerning a novel SHM strategy. A stiffened carbon-epoxy plate regarding a healthy condition and simulated damage on the center of the bottom part of the stiffener is utilized. Comparing the performance in terms of simulation errors is made to observe if the identified models can represent and predict the waveform with confidence bounds considering the confounding effect produced by noise or possible temperature variations assuming a dataset preprocessed using principal component analysis. The results of the GP-NARX identified model have attested correct classification with a reduced number of false alarms, even with model uncertainties propagation regarding healthy and damaged conditions.

Numerical investigations into Nonlinear Vibro-ultrasonics and Surface Vibration Comparison method for detection of defects in a composite laminate

This paper begins with a numerical study based on earlier experiments of nonlinear vibro-ultrasonic behaviour of a composite laminate with a delamination defect upon sinusoidal linear sweep signal excitation . A methodology to model laminates with cross-ply layup is presented which can be extended to any layup if desired. In comparison to experiments where it is challenging to visualize the fine details of vibrations, simulations make it easier to visualize and helps in optimizing the defect probing methods. The paper goes on to discuss with the help of numerical results that a separation gap between the delamination surfaces occurs to be a common cause for the failure of nonlinear vibro-ultrasonic methods to detect delamination defects. This constraint can often be overcome with application of higher excitation amplitudes as has been demonstrated in several experimental works. However in this study, a new approach named Surface vibration comparison method (SVCM) to probe delamination defects in the absence of contact acoustic nonlinearity is proposed as a proof-of-concept. The technique is then evaluated for detection of weak kissing bond defects in composite beam specimens. Both the experimental and simulation results show potential of the method as damage detection technique in thin composite structures.

ALKALI ACTIVATED FLY ASH BASED CONCRETE: EVALUATION OF CURING PROCESS USING NON-LINEAR ULTRASONIC APPROACH

This paper investigates the applicability of nondestructive testing and evaluation (NDT&E) method using ultrasonic signals to monitor the curing of alkali activated fly ash based concrete (AAFC). The evaluation was carried out on AAFC specimens with two different water/binder (W/B) ratios of 0.3 and 0.5 and after curing at 60 °C for 7, 14 and 28 days, respectively. The signals are recorded and analyzed using linear and non-linear ultrasonic techniques. The results show that the non-linear ultrasonic technique has a clear advantage over the linear ultrasonic technique when monitoring the curing of AAFC specimens with the lower W/B ratio. However, the specimens with the higher W/B ratio do not undergo proper curing and therefore do not show clear distinctions between the curing times measured from the two ultrasonic techniques. The unconfined compressive strength (UCS) of the AAFC specimens at different W/B ratios and curing times is also measured. The UCS results showed a good correlation with the ultrasonic results.

Vibration monitoring of nuclear power plant containment buildings during the Integrated Leakage Rate Test for structural condition assessment

This study investigates the use of the operational vibrations produced during the Integrated Leak Rate Test of nuclear power plant containment buildings for further informing on its mechanical condition. The experiment is performed on a 1:3-scale containment building mock-up. The results show that meaningful vibrations were generated during the pressurization test. Different features were extracted from the vibration signals and analyzed as a function of the internal pressure. Experimental modal analysis was performed and demonstrated that several frequency peaks generated during the pressurization cycle effectively corresponded to the eigenmodes of the containment building. The identified operational frequency modes exhibited remarkable hysteretic dependencies on the internal pressure. The latter was phenomenologically described through a simplified 2D Finite Element Model of the vessel. Besides, a surrogate statistical model based on the Principal Component Analysis of the vibration data was proposed as a baseline and so detect abnormal behavior. Then, different synthetic damage scenarios were created by subtlety altering the recorded signals and ultimately substantiate the capability of the statistical model to detect these odd signals. Finally, conclusions were drawn regarding the possibility of using mechanical vibrations for assisting in the licensing process of nuclear power plants and monitor the structural health condition of in-service containment buildings.

Highly Sensitive Nonlinear Identification To Track Early Fatigue Signs in Flexible Structures

This study describes a physics-based and data-driven nonlinear system identification approach for detecting early fatigue damage due to vibratory loads. The approach also allows for tracking the evolution of damage in real-time. Nonlinear parameters such as geometric stiffness, cubic damping and phase angle shift can be estimated as a function of fatigue cycles, which are demonstrated experimentally using flexible aluminum 7075-T6 structures exposed to vibration. Nonlinear system identification is utilized to create and update nonlinear frequency response functions, backbone curves and phase traces to visualize and estimate the structural health. Findings show that the dynamic phase is more sensitive to the evolution of early fatigue damage than nonlinear parameters such as the geometric stiffness and cubic damping parameters. A modifed Carrella-Ewins method is introduced to calculate the backbone from the nonlinear signal response, which is in good agreement with the numerical and harmonic balance results. The phase tracing method is presented, which appears to detect damage after approximately 40% of fatigue life, while the geometric stiffness and cubic damping parameters are capable of detecting fatigue damage after approximately 50% of the life-cycle.

A Development Strategy for SHM Applications

Permanently installed SHM systems are now a viable alternative to traditional periodic inspection (NDT). However, their industrial use is limited and this paper reviews the steps required in developing practical SHM systems. The transducers used in SHM are fixed in location, whereas in NDT they are generally scanned. The aim is to reach similar performance with high temporal frequency, low spatial frequency SHM data to that achievable with conventional high spatial frequency, low temporal frequency NDT inspections. It is shown that this can be done via change tracking algorithms such as the Generalized Likelihood Ratio (GLR) but this depends on the input data being normally distributed, which can only be achieved if signal changes due to variations in the operating conditions are satisfactorily compensated; there has been much recent progress on this topic and this is reviewed. Since SHM systems can generate large volumes of data, it is essential to convert the data to actionable information, and this step must be addressed in SHM system design. It is also essential to validate the performance of installed SHM systems, and a methodology analogous to the model assisted POD (MAPOD) scheme used in NDT has been proposed. This uses measurements obtained from the SHM system installed on a typical undamaged structure to capture signal changes due to environmental and other effects, and to superpose the signal due to damage growth obtained from finite element predictions. There is a substantial research agenda to support the wider adoption of SHM and this is discussed.

Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation with Deep Neural Networks

Composite materials can be modified according to the requirements of applications and hence their applications are increasing significantly with time. Due to the complex nature of the aging of composites, it is equally challenging to establish structural health monitoring techniques. One of the most applied non-destructive techniques for this class of materials is using Lamb waves to quantify the damage. Another important advancement in damage detection is the application of deep neural networks. The data-driven methods have proven to be most efficient for damage detection in composites. For both of these advanced methods, the burning question always has been the requirement of data and quality of data. In this paper, these measurements were used to create a framework based on a deep neural network for efficient fault diagnostics. The research question developed for this paper was: can data fusion techniques used along with data augmentation improve the damage diagnostics using the convolutional neural network? The specific aims developed to answer this research question were (1) highlighting the importance of data fusion methods, (2) underlining the importance of data augmentation techniques, (3) generalization abilities of the proposed framework, and (4) sensitivity of the size of the dataset. The results obtained through the analysis concluded that the artificial intelligence techniques along with the Lamb wave measurements can efficiently improve the fault diagnostics of complex materials such as composites.

MACHINE LEARNING AND ANOMALY DETECTION ALGORITHMS FOR DAMAGE CHARACTERIZATION FROM COMPLIANCE DATA IN 3-POINT BENDING FATIGUE

Three-point bending fatigue compliance datasets of multi-layer fiberglass-weave/epoxy test specimens, including five and ten mil interlayers, were analyzed using Artificial Intelligence (AI) methods along with statistical analysis, revealing the existence of three different compliance-based damage modes. Anomaly detection algorithms helped discover damage indicators observable in short intervals (of 50 cycles) in the compliance data, whose patterns vary with the material and the number of load cycles to which the material is subjected. Machine learning algorithms were applied using the compliance features to assess the likelihood that material failure may occur within a certain number of future loading cycles. High accuracy, precision, and recall rates were achieved in the classification task, for which we evaluated several algorithms, including various variations of neural networks and support vector machines. Thus our work demonstrates the utility of AI algorithms for discovering a diversity of damage mechanisms and failures.