Detection Of Subsurface Damage Research Articles

Subsurface damage detection and prediction of thin-walled complex curved-surface component

The hemispherical resonant gyroscope (HRG), distinguished by its simple structure, high reliability, and remarkable precision, stands out as one of the most promising high-performance inertial navigation devices in advanced equipment. However, the core sensitive component of the HRG, known as the hemispherical resonator (HSR), with a complex and small curvature structure made of fused silica, experiences subsurface damage (SSD) during the grinding process. This not only leads to brittle fracture, which significantly hampers the polishing efficiency of the component but also affects the operational performance of the gyroscope. This study was initiated by quantitatively characterizing the SSD of various structural features of the HSR, including the center rod, lip edge, and spherical shell. Especially, for the curved-surface of the HSR, the average deviation of SSD detection result was within 6%. Subsequently, based on the characterized SSD, the grinding damage mechanisms of the HSR were explored. The grinding surface integrity of the spherical shell was then elucidated from the perspective of grinding stiffness and a theoretical model of subsurface damage (SSD) is derived. Finally, a predictive model for the SSD depth of the HSR after ultra-precision grinding was established according to the nonlinear relationship between the surface roughness and SSD depth. Furthermore, the maximum height of the grinding surface (Sz) could be used to further characterize the SSD depth of the ground surface for the HSR, showing a high level of predictive accuracy. This work fills the blank in SSD detection of the HSR, laying a theoretical foundation for the high-performance manufacturing of the HSR.

A review of subsurface damage detection methods for optical components

Subsurface damage (SSD) induced during the processing of optical components, which are key parts of large optical systems, can deteriorate optical system performance or damage the complete system. To eliminate the SSD of optical components in subsequent processing, it must be accurately detected. This paper presents a review of the principle, characteristics, research status, and development trends of SSD detection methods for optical components. This review finds that destructive detection methods can damage the components and cannot detect SSD online, but they are reliable and accurate and characterized by facile principles, easy implementation, and inexpensive equipment. Consequently, such methods are widely used for the validation of non-destructive methods and quality control of components in industrial production. In comparison, non-destructive detection methods can detect SSD online without destroying the components, shorten the detection cycle, increase the overall production efficiency, and decrease the cost. Therefore, non-destructive detection methods have promising development prospects.

Numerical study for development of subsurface crack detection using pulsed eddy current and swept frequency eddy current

Root-deck crack, a type of subsurface damage, is one of the most concerning fracture issues on orthotropic steel decks. Therefore, the development of novel and easy-to-use subsurface damage detection technique is crucial to detect damage at an early stage to prevent the occurrence of major damage. In this paper, new methods to process the response signal of the eddy current technique (ECT) using the normalised peak and time to zero crossing of the change in induced voltage, and the peak and frequency at the peak of the normalised change in phase angle, are proposed. Based on these newly proposed methods, a probe configuration optimisation study was carried out using dynamic electromagnetic field analysis. From numerical studies, the cup-core probe promises high capability to detect damage located 10 mm below the surface of a 12 mm steel plate and maintain the sensitivity at an eccentric position. Furthermore, the newly proposed signal processing methods enable users to interpret response signals for subsurface damage detection in a more comprehensive way than conventional methods.

Polarized laser scattering detection of subsurface damage of quartz glass induced by grinding

Subsurface damage (SSD) is inevitably generated during the grinding process of quartz glass. It has a great impact on sustainability, lifetime and optical performance of quartz glass components and systems. This paper proposes a non-destructive detection method for detecting SSD in ground quartz glass based on polarized laser scattering (PLS). A PLS system was built to detect SSD in ground quartz glass samples prepared with different abrasive particle sizes. The PLS detection signal value had a positive correlation with the SSD depth. The finite-difference time-domain (FDTD) method was used to simulate the laser scattering process at the SSD. The distribution of electric field intensity could reflect the location of SSD. It is concluded that the PLS system can effectively detect SSD in the ground quartz glass.

Subsurface damage detection via noncontact laser based surface level strain sensing smart skin with carbon nanotubes

Subsurface damage may remain undetected for long periods and accumulate until it leads to structural failure. Non-destructive measurements of surface strain can provide critical data for detecting early symptoms of hidden damage and helping to prevent serious consequences. This study investigates the capabilities of the developed method called strain-sensing smart skin (S4) to find internal damage. The S4 method uses single-wall carbon nanotubes in a thin polymer coating as optically interrogated fluorescent strain sensors to measure the strain map. A set of aluminum specimens with internal cavities representing damage were coated both with S4 films and with digital image correlation (DIC) speckle patterns. Maps of surface strain from the two methods were then compared (after the application of tensile stress to the specimens) to compute finite element method (FEM) strain maps. The strain maps obtained from S4 point-wise laser scanning agreed well with FEM predictions, while DIC strain maps were not very precise. S4 maps revealed strain map features with superior accuracy. It is concluded that S4 strain mapping can be used as a non-destructive evaluation method for detecting and visualizing subsurface damage in structural elements for structural health monitoring.

Subsurface damage detection of optical elements by analyzing the photobleaching properties of quantum dots

Tagging the lapping and polishing slurries with quantum dots (QDs) is a promising method for detecting the distribution and depth of subsurface damage (SSD) in optical element at the same time. However, QDs are prone to photobleaching which affects the detection accuracy of SSD distribution and depth. The photobleaching properties of QDs with different structure and composition are different. This paper proposes a SSD detection method of optical element by analyzing the photobleaching properties of QDs. C QDs, CdSe QDs, CdSe/ZnS QDs, InP/ZnS QDs, and CuInS2/ZnS QDs were selected to tag SSD of optical element. A fluorescence detection system for SSD was developed to collect fluorescence images of QD-tagged optical elements under various excitation intensities, excitation time, and excitation numbers. The photobleaching properties of the QDs in SSD of optical elements were quantitatively analyzed by two parameters: (1) fluorescence conversion efficiency under various excitation intensities and (2) fluorescence stability index under various excitation time and excitation numbers. The distribution and depth of SSD were detected to comprehensively analyze the influence of the photobleaching properties of QDs on the SSD detection accuracy. The results show that the anti-photobleaching properties of C QDs, InP/ZnS QDs, CdSe/ZnS QDs, and CuInS2/ZnS QDs were strong, and they can detect SSD distribution and depth with high accuracy; while the anti-photobleaching property of CdSe QDs was poor, and they can detect SSD distribution and depth with low accuracy, making them unsuitable for tagging SSD. For CdSe/ZnS QDs, they can be used to detect SSD accurately only when the excitation intensity was higher than 33 mW. This paper lays a foundation for investigating the formation mechanism of SSD and detecting SSD non-destructively and accurately.

Rapid subsurface damage detection of SiC using inductivity coupled plasma

This paper proposes a method for the rapid detection of subsurface damage (SSD) of SiC using atmospheric inductivity coupled plasma. As a plasma etching method operated at ambient pressure with no bias voltage, this method does not introduce any new SSD to the substrate. Plasma diagnosis and simulation are used to optimize the detection operation. Assisted by an SiC cover, a taper can be etched on the substrate with a high material removal rate. Confocal laser scanning microscopy and scanning electron microscope are used to analyze the etching results, and scanning transmission electron microscope (STEM) is adopted to confirm the accuracy of this method. The STEM result also indicates that etching does not introduce any SSD, and the thoroughly etched surface is a perfectly single crystal. A rapid SSD screening ability is also demonstrated, showing that this method is a promising approach for the rapid detection of SSD.

Subsurface damage detection and structural health monitoring using digital image correlation and topology optimization

Detecting subsurface defects in structural members is a challenging yet important part of condition assessment of structures. Existing methods in this regard are either based on nondestructive evaluation and testing (NDE/T), or structural health monitoring (SHM) concepts. NDE/T methods require expensive equipment usually based on wave propagation or radiation imaging. Damage detection based on SHM usually relies on global vibration response which has proven successful in informing the existence and sometimes coarse-grained location information about damage, but is fairly limited in reconstructing the 3D shape of internal damage. This paper proposes to leverage full-field response data obtained by digital image correlation (DIC) in a topology optimization framework to reconstruct the internal damage in members. In other words, this paper shows how perturbations in observable full-field surface measurements can be used as a proxy to detect unobservable internal abnormalities.An initial finite element model of the structure is first created to discretize the member into elements whose constitutive properties are treated as unknowns in the optimization problem. The goal of the optimization is to minimize the discrepancies between the observed full-field response measured experimentally using DIC, and that computed numerically using the model. To that end, an objective function is first computed as the sum of residuals by mapping both responses onto a common grid, which is then pushed to a minimum via the method of moving asymptotes (MMA) as the optimization algorithm.The framework was evaluated on a series of simulated and real-world experiments using steel coupon specimens with artificially manufactured defects. Results show that the proposed method is capable of detecting and reconstructing the location, extent, and shape of the damage with average F1-scores of 82.8% and 69.6% on simulated and real experiments, respectively. Furthermore, a detailed sensitivity analysis demonstrated the effect of various factors on the performance of the proposed approach, including different optimization starting points, defect severity, sensing density, and discretization density. Results from this study have demonstrated that the proposed method is able to successfully extract detailed internal damage information that is otherwise expensive and difficult to achieve with state-of-the-art methods and can therefore be used as a promising subsurface damage detection method.

Two-dimensional detection of subsurface damage in silicon wafers with polarized laser scattering

Abstract This study proposes a polarized laser scattering (PLS) method for detection of the two-dimensional distribution of the grinding-induced subsurface damage (SSD). This study also uses two destructive methods to observe the distribution of SSD in the ground silicon wafers by cross-sectional microscopy and by layer-by-layer microscopy. An experimental investigation is arranged for detecting the two-dimensional damage from diamond wheel grinding of silicon wafers through wafer sample preparation and SSD observations with the cross-sectional and layer-by-layer microcopy. It also includes PLS detection of the ground silicon wafers for SSD information. The results show that both the depth of SSD and the crack density contribute to the PLS detection signals. The study reveals the characteristics of the PLS detection, which illustrates the potential application of the PLS method to in-process detection.

Subsurface Damage Detection on Ground Silicon Wafers Using Polarized Laser Scattering

Abstract A silicon wafer is important for the electronic and computer industries. However, subsurface damage (SSD), which is detrimental to the performance and lifetime of a silicon chip, is easily induced in a silicon wafer during a grinding process since silicon is typically a hard and brittle material. Therefore, it is necessary to detect and remove SSD in the subsequent processes. In this study, a polarized laser scattering (PLS) system is installed to detect the SSD in a ground wafer. It is found that not only the subsurface crack but also the residual stress leads to depolarization of an incident light. The effects of residual stress on depolarization are studied. The residual stress results in the photoelasticity, which causes the depolarization of the incident light in the PLS system. The depolarization caused by the residual stress is determined by the directions and the difference of the principal stresses. When the polarization direction of the incident light is aligned with one of the principal stresses, the effects of the residual stress can be minimized; therefore, the subsurface crack can be quantitatively estimated by PLS.

Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling

Bearings are the culprit of a large quantity of Wind Turbine (WT) gearbox failures and account for a high percentage of the total of global WT downtime. Damage within rolling element bearings have been shown to initiate beneath the surface which defies detection by conventional vibration monitoring as the geometry of the rolling surface is unaltered. However, once bearing damage reaches the surface, it generates spalling and quickly drives the deterioration of the entire gearbox through the introduction of debris into the oil system. There is a pressing need for performing damage detection before damage reaches the bearing surface. This paper presents a methodology for detecting sub-surface damage using Acoustic Emission (AE) measurements. AE measurements are well known for their sensitivity to incipient damage. However, the background noise and operational variations within a bearing necessitate the use of a principled statistical procedure for damage detection. This is addressed here through the use of probabilistic modelling, more specifically Gaussian mixture models. The methodology is validated using a full-scale rig of a WT bearing. The bearings are seeded with sub-surface and early-stage surface defects in order to provide a comparison of the detectability at each level of a fault progression.

Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer

A new deep learning-based method is proposed to detect subsurface damage of steel members in a steel truss bridge using infrared thermography (IRT). To reduce computation costs, the original deep inception neural network (DINN) is modified for transfer learning. The proposed method provides bounding boxes for detecting and localizing subsurface damage such as corrosion and debonding between paint with coating and steel surface. Robustness and accuracy were tested on 200 thermal images (640 × 480 pixels), and 96% accuracy and 97.79% specificity was achieved. The results were validated with ultrasonic pulse velocity (UPV) tests.

Methods for Detection of Subsurface Damage: A Review

Subsurface damage is easily induced in machining of hard and brittle materials because of their particular mechanical and physical properties. It is detrimental to the strength, performance and lifetime of a machined part. To manufacture a high quality part, it is necessary to detect and remove the machining induced subsurface damage by the subsequent processes. However, subsurface damage is often covered with a smearing layer generated in a machining process, it is rather difficult to directly observe and detect by optical microscopy. An efficient detection of subsurface damage directly leads to quality improvement and time saving for machining of hard and brittle materials. This paper presents a review of the methods for detection of subsurface damage, both destructive and non-destructive. Although more reliable, destructive methods are typically time-consuming and confined to local damage information. Non-destructive methods usually suffer from uncertainty factors, but may provide global information on subsurface damage distribution. These methods are promising because they can provide a capacity of rapid scan and detection of subsurface damage in spatial distribution.

A data processing methodology for infrared thermography images of concrete bridges

This study presents a methodology to improve the usability and efficiency of infrared thermography (IRT) for subsurface damage detection in concrete structures. A practical and more objective approach to obtain a threshold for IRT data processing was developed by incorporating finite element (FE) model simulations. Regarding the temperature thresholds of sound and delaminated areas, the temperature of the sound part was obtained from the IR image, and the temperature of the delaminated area was defined by FE model simulation. With this methodology, delaminated areas of concrete slabs at 1.27cm and 2.54cm depths could be detected more objectively than by visually judging the color contrast of IR images. However, it was also found that the boundary condition affects the accuracy of the method, and the effect varies depending on the data collection time. On the other hand, it can be assumed that the influential area of the boundary condition is much smaller than the area of a bridge deck in real structures; thus, it might be ignorable on real concrete bridge decks. Even though there are some limitations, this methodology performed successfully paving the way towards automated IRT data analysis for concrete bridge deck inspections.

Investigation of effective utilization of infrared thermography (IRT) through advanced finite element modeling

Infrared thermography (IRT) has been used experimentally for concrete delamination detection. The past studies were conducted with limited experimental conditions, which make a difference in delamination detection. As a result, there are inconsistencies in the results reported in the literature. In this study, heat transfer models of concrete blocks with artificial delamination used for a previous test are developed and analyzed to explore sensitive parameters for effective utilization of IRT. After these FE models are validated, critical parameters and factors of delamination detectability such as the size of delamination (area, thickness and volume), ambient temperature and solar irradiance conditions (different seasons), and the depth of delamination from the surface are explored. This study presents that the area of delamination is much more influential in the detectability of IRT than thickness and volume. It is also found that there is no significant difference depending on the season when IRT is employed. This study shows a potential to bring significant improvement to IRT use in the field for subsurface damage detection for concrete structures.

RB-SiC亚表面损伤检测及其旋转超声磨削亚表面损伤特征

RB-SiC亚表面损伤检测及其旋转超声磨削亚表面损伤特征

Monitoring concrete bridge decks using infrared thermography with high speed vehicles

There is a need for rapid and objective assessment of concrete bridge decks for maintenance decision making. Infrared Thermography (IRT) has great potential to identify deck delaminations more objectively than routine visual inspections or chain drag tests. In addition, it is possible to collect reliable data rapidly with appropriate IRT cameras attached to vehicles and the data are analyzed effectively. This research compares three infrared cameras with different specifications at different times and speeds for data collection, and explores several factors affecting the utilization of IRT in regards to subsurface damage detection in concrete structures, specifically when the IRT is utilized for high-speed bridge deck inspection at normal driving speeds. These results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. It is observed that nighttime would be the most suitable time frame with less false detections and interferences from the sunlight and less adverse effect due to direct sunlight, making more "noise" for the IRT results. This study also revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution.

Modeling of Environmental Effects on Thermal Detection of Subsurface Damage in Concrete

A significant form of deterioration in concrete is corrosion of embedded reinforcing steel that can cause subsurface delaminations and spalling. Infrared thermography can be used to detect delaminations based on variations in surface temperature that are caused by the disruption of the heat flow through the delaminated area. The surrounding environmental conditions such as sunlight, ambient temperature variation, and wind speed are critical for heat transfer, and as such the technology depends on these environmental conditions. This paper describes a numerical model developed to predict thermal contrasts for subsurface delaminations based on a given set of environmental conditions surrounding the concrete. The finite element method (FEM) was used to perform 3-D nonlinear transient heat-transfer analysis of a large concrete block with embedded Styrofoam targets intended to provide an idealized model of subsurface delaminations. The effectiveness of the modeling was evaluated by comparing the thermal contrasts predicted by the model and those obtained from experimental testing of an actual concrete block of the same dimensions. The correlation and error between the experimental testing and the model results indicated that the model could be an effective tool for the prediction of anticipated thermal contrasts based on given weather conditions.

Holography and associated laser techniques in the world of the museum and archaeology

This paper discusses the application of pulsed laser recording for three-dimensional display of ancient artefacts, medical dissections for hospital museum exhibition and also non-destructive testing techniques for the detection of subsurface damage on such items such as paintings or frescos and other ancient artefacts where debonding between surfaces and their supporting background may have occurred. It also considers the information storage potential of holography that could be of use for museum applications. A further associated laser technique is also discussed which detects defects in ancient metal structures; the latter technique was used on the Marco Aurelio equestrian bronze, located in Rome.

Speckle interferometry for detection of subsurface damage in fibre-reinforced composites

A recently reported new technique renders speckle interferometry applicable for measuring high local displacement gradients. This paper reports how the technique is used to detect and quantify damage in fatigued carbon fibre-reinforced plastic laminates via its effect on surface displacements. Moreover, the measured displacement fields are used to verify a finite element damage model. The accuracy of the delamination measurement is confirmed by ultrasonic results.