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Active 3D thermography combines 3D shape and active thermography. Thus, it enables the provision of the intuitive thermographic inspection results for composite parts with a complex geometry. However, conventional 3D thermography acquires thermographic information and 3D shape through at least two independent sensors and requires complex cross-modal image registration algorithm based on the keypoint detection and matching. In this paper, an active 3D thermography system is proposed using only one thermal camera for moving objects. This system does not require an independent 3D sensor while the thermal camera acts as the 3D sensor. Nature behind that is that a mathematical model is proposed to unify the line scanning thermography and laser triangulation in the dynamic scanning process, taking a line laser as both heat excitation for active thermography and spatial coding for 3D reconstruction. Furthermore, the model enables the feature-free registration of thermogram sequence and 3D shape, so that the registration is fast and robust without keypoint features. The experiments on standard height specimens, 3D printing glass fiber composites, and carbon fiber intake tube have shown the error is calibrated within 0.25 mm in the range of 1 mm to 150 mm and evidenced the capability for sub-surface defects detection.

Joint scanning laser thermography (JLST) is well-known for its efficiency to overcome the field of view (FOV) limitation of thermal imagers. However, JSLT requires a data reconstruction to reveal the location of the defective area straightforwardly. Moreover, its detection capacity is limited by the lack of a deconvolution algorithm adaptive to the reconstructed data. In this study, a deconvolutional reconstruction method based on the Lucy–Richardson (LR) algorithm has been developed for JST, which is effective in suppressing random noise and the blur effect caused by the thermal diffusion. A JSLT inspection is carried out on a functional coating material with cylinder-like defects to test the performance of the proposed method. In comparison to the directly processed method on the original data, the proposed method is processed on the reconstructed data and then compared with principal component analysis (PCA), restored pseudo heat flux (RPHF), fast Fourier transform (FFT) methods and non-negative matrix factorization (NMF). The experimental results indicated that our proposed LR method exhibited a higher signal-to-noise ratio. Besides, it can detect the cylinder-mocked debonding defects with a diameter of 1.5 mm and a depth of 2.0 mm buried under the 1.0-mm coating. In addition, the defect detection diameter-to-depth ratio reached 1.5, while the defect detection rate of the test specimens can approach 90%.

In this paper, a new inspection method, joint scanning laser thermography (JSLT) as well as its data reconstruction and processing algorithm, is proposed. The new inspection method is utilized to detect and characterize the flat-bottom holes (FBH) in carbon fiber composites by using joint laser scanning scheme. By analyzing the nature of the thermal image sequences sampled under such a scanning scheme, a quick and simple reconstruction method is developed to characterize the buried depth of defects based on 1D heat conduction model. The processed thermal images are expected to get higher temporal resolution and spatial resolution. It can inspect larger area within shorter acquisition time than the pulse thermography. Thus, the study solves the dilemma between the inspection speed and the inspection capacity. Later, a joint laser scanning thermography test is set up to test the algorithm on a carbon fiber composite panel with defects buried at different depth. The experimental results show that the reconstructed data almost behave as those under pulse excitation. The tendency of temperature to change in the logarithmic domain over time is similar to the curve in the TSR method. But, unlike the pulse thermography data, the defect detection rate of PCA based on reconstructed data is higher than that of fast Fourier transform (FFT) amplitude image, independent component analysis (ICA) and FFT phase image. The JSLT system is used to detect FBHs and the diameter-depth ratio reached 3.33.

This paper presents the design of a 2x4 rectangular patch antenna array for 5G Applications. The antenna array is designed using Rogers RT/Duroid 5880 as substrate with height 1.575mm, dielectric constant of 2.2 and loss tangent of 0.0009. A prototype of the single patch element and the eight-elements array are manufactured to validate simulated results. The single inset-fed patch antenna attained a gain of 7.6 dB while the antenna array exhibited a peak gain of 16.2 dB. The 10 dB bandwidth of the array covers from 3400 MHz to 3520 MHz, making it suitable for Band n78 5G access points application.

Thermogram reconstruction methods based on one-dimensional models are widely used in data processing for thermography inspections. However, the surface temperature variances caused by thermal diffusion will be compatible with those caused by delamination whose normalized aspect (diameter-to-depth) ratio is close to 1 when the defect itself is very thin, about 0.15 mm in this research. This phenomenon makes the detection capacity of these methods reduced at such defects with small aspect ratios. The paper proposes a new reconstruction method, phase-locked restored pseudo heat flux (RPHF), for thermography inspection using square-wave optical stimulations. The theoretical analysis shows the independence of the method upon the effect of thermal diffusion blur at defect-free areas. Square-wave thermography tests are conducted on a carbon fiber composite panel with artificial delimitations buried up to 4 mm deep. The method is implemented on a private computer to deconvolute the RPHF kernel from the transformed thermogram data. The data are separated into two sets with a one-period phase shift to each other sequentially; a phase-locked substation is applied between the sets. The global signal-to-noise ratios obtained with the proposed method are compared to those obtained with Busse's lock-in phase images and those with thermographic signal reconstruction. The phase-locked RPHF gives the best global signal-to-noise ratios for normalized aspect ratio at 1.1 when sufficient heat is applied. It's concluded that the thermal diffusion effect at defect-free areas should be considered in thermography inspection for defects with a normalized aspect ratio at 1.1.

Impact damage, caused by low-energy impact, is inevitable during the whole life time of carbon fiber reinforced plastic (CFRP) material. However, the barely visible impact damage (BVID) is difficult to be detected by visual methods. Ultrasonic thermography (UT) is an emerging nondestructive testing technique that visualizes damage in thermal images captured by an infrared (IR) camera when the material is stimulated by ultrasound. However, noise and blurry edges around the high-temperature areas may cause confusion and lead to unreliable results in the thermal images of UT test. In this paper, an impact damage inspection method is proposed based on manifold learning for the CFRP material. Low-power ultrasonic excitation is used for this UT. The IR image sequences are processed as datasets in high-dimensional space. These datasets are reduced to lower dimensions by manifold learning to find the intrinsic structure in the two-dimensional manifold. Each dimension of the embedding manifold correlates highly with one degree of freedom underlying the original pixel: steady and random components. The steady component, which reflects the temperature rise caused by damage, is used for VID and BVID detection. The experimental system was set up, and CFRP plate specimens with different impact damage were tested. All the impact damage could be detected and shown in reconstructed static image with little noise. The proposed method using image sequences could provide a visualized, reliable, and effective impact damage inspection and localization means for CFRP material during manufacturing and in service.

To improve bone regeneration in oral microenvironment, we generated a novel biodegradable, antibacterial, and osteoconductive electrospun PLGA/PCL membrane as an ideal osteogenic scaffold. The novel three-layer membranes were structured with serial layers of electrospun chlorhexidine-doped-PLGA/PCL (PPC), PLGA/PCL (PP), and β-tricalcium phosphate-doped-PLGA/PCL (PPβ). To characterize osteoconductive properties of these membranes, MC3T3-E1 (MC) cultures were seeded onto the membranes for 14 days for evaluation of cell proliferation, morphology and gene/protein expression. In addition, MC cells were cultured onto different surfaces of the three-layer membranes, PPC layer facing MC cells (PPβ-PP-PPC) and PPβ layer facing MC cells (PPC-PP-PPβ) to evaluate surface-material effects. Membrane properties and structures were evaluated. Antibacterial properties against Streptococcus mutans and Staphylococcus aureus were determined. Scanning electron microscope demonstrated smaller interfiber spaces of PPC and PPβ-PP-PPC compared to PPβ, PPC-PP-PPβ, and PP. PPC and PPβ-PP-PPC exhibited hydrophilic property. The three-layer membranes (PPC-PP-PPβ and PPβ-PP-PPC) demonstrated significantly higher Young's modulus (94.99 ± 4.03 MPa and 92.88 ± 4.03 MPa) compared to PP (48.76 ± 18.15 MPa) or PPC (7.92 ± 3.97 MPa) (p0.05). No significant difference of cell proliferation was found among any groups at any time point (p0.05). Higher expression of integrins were detected at 12 h of cultures on PPC-PP-PPβ compared to the controls. Promoted osteoconductive effects of PPC-PP-PPβ were revealed by alkaline phosphatase assays and Western blot compared with the controls at 7 and 14 days. PPC, PPC-PP-PPβ and PPβ-PP-PPC exhibited a significantly wider antibacterial zone against the tested bacteria compared to PP and PPβ (p0.05). These results suggested that the three-layer electrospun membranes demonstrated superior properties: higher strength, better cell adhesion, and promoted osteoconductive properties compared to single-layer membrane: however, antibacterial properties were exhibited in three-layer electrospun membranes and chlorhexidine-doped single-layer membrane. We concluded that the novel three-layer membranes could be used as a biocompatible scaffold for intraoral bone regeneration due to its enhanced osteoconductive activity and antibacterial effect.

Carbon fibre composite materials are widely used in high-value, high-profit applications – such as aerospace manufacturing and shipbuilding – due to their low density, high mechanical strength, and flexibility. However, existing techniques for non-destructive evaluation of composite materials are limited and not robust. Thermography has been demonstrated to be a promising technology; however, thermal diffusion and uncontrolled uneven heating considerably degrade its capacity to detect thin planar defects in carbon fibre composites. In this study, we have developed a method, restored pseudo heat flux (RPHF), for addressing this problem. Unlike many existing methods, RPHF does not require prior knowledge about defect depth. A numerical simulation was developed, and three sets of experiments were performed using a layered sample. Results suggest that in comparison with other existing methods, RPHF does not require an accurate value for its input, thermal diffusivity. It provides a good approximation ...

Different proportions of Al and Cr alloying MoSi2 were prepared by arc cladding with different mass fractions of MoSi2, Al, and Cr as raw materials. The work studied the effects about of Al and Cr on the phase, high-temperature oxidation morphology, products, and high-temperature oxidation property of MoSi2. The results confirmed that the volume expansion coefficient of Al2O3 generated by material oxidation was low which could reduce the degree of mismatch and the cracks in the oxide film. Cr element had a poor effect on enhancing the continuity and compactness of the oxide film, however, it could improve the stability of t-MoSi2 formation after arc cladding. MoO3 was not formed in the MoSi2 material added with Al and Cr show the surface oxidation. A dense and continuous Al2O3–SiO2 oxide film was formed, and no cracks holes were found in the oxide film. After oxidation at 800 °C–1200 °C for 120 h, the sample still maintained good oxidation protection. According to the calculation of oxidation kinetics, the MoSi2 material added with Al and Cr had good oxidation resistance. 3% Al + 9% Cr + 88% MoSi2 had the optimal high-temperature oxidation resistance about four times than pure MoSi2.

A method without requirements on knowledge about thermal properties of coatings or those of substrates will be interested in the industrial application. Supervised machine learning regressions may provide possible solution to the problem. This paper compares the performances of two regression models (artificial neural networks (ANN) and support vector machines for regression (SVM)) with respect to coating thickness estimations made based on surface temperature increments collected via time resolved thermography. We describe SVM roles in coating thickness prediction. Non-dimensional analyses are conducted to illustrate the effects of coating thicknesses and various factors on surface temperature increments. It’s theoretically possible to correlate coating thickness with surface increment. Based on the analyses, the laser power is selected in such a way: during the heating, the temperature increment is high enough to determine the coating thickness variance but low enough to avoid surface melting. Sixty-one pain-coated samples with coating thicknesses varying from 63.5 μm to 571 μm are used to train models. Hyper-parameters of the models are optimized by 10-folder cross validation. Another 28 sets of data are then collected to test the performance of the three methods. The study shows that SVM can provide reliable predictions of unknown data, due to its deterministic characteristics, and it works well when used for a small input data group. The SVM model generates more accurate coating thickness estimates than the ANN model.

Apatite-type Lanthanum silicate (LSO) is among the most promising electrolyte for intermediate temperature solid oxide fuel cells (IT-SOFCs) owing to the high conductivity and low activation energy at lower temperature than traditional doped-zirconia electrolyte. The ionic conductivity as well as the sintering density of lanthanum silicate oxy-apatite, La 10 Si 6- x Cu x O 27-δ (LSCO, 0 ≤ x ≤ 2), was effectively enhanced through a small amount of doped copper. The phase composition, relative density, ionic conductivity and thermal expansion behavior of La 10 Si 6- x Cu x O 27-δ was systematically investigated by X-ray diffraction (XRD), Archimedes' drainage method, scanning electron microscope (SEM), electrochemical impedance spectra (EIS) and thermal dilatometer techniques. With increasing copper doping content, the ionic conductivity of La 10 Si 6- x Cu x O 27-δ increased, reaching a maximum of 4.8 × 10 −2 S cm −1 at 800 °C for x = 1.5. The improved ionic conductivity could be primarily associated with the enhanced grain conductivity. The power output performance of NiO-LSCO/LSCO/LSCF single cell was superior to that obtained on NiO-LSO/LSO/LSCF at different temperatures using hydrogen as fuel and oxygen as oxidant, which could be attributed to the enhanced oxygen ionic conductivity as well as the sintering density for the copped doped lanthanum silicate. In conclusion, the apatite La 10 Si 4.5 Cu 1.5 O 25.5 is a promising candidate electrolyte for IT-SOFCs.

The study here demonstrates the capability of thermography to detect and characterize pinhole defects in a visually transparent anti-reflection (AR) film. The diameter of the pin-holes varies from 0.03 mm to 4 mm. Each inspected area was unevenly heated for 65 s. Thermal images were processed by the following steps: de-trend processing, primary edge detection based on Sobel approximation, and further edge separation based on k-means clustering. Then, the diameter of pinholes was directly measured from binary images. The proposed image processing enables thermography to detect 86 of 90 inspected areas correctly in position.

In this paper, terahertz (THz) imaging and vibro-thermography (VT) are implemented for non-destructive evaluation (NDE) of CFRP subjected to impact damage. THz inspection is well-known for its outstanding detection depth in dielectric materials but unreliable in conductive materials like carbon fiber reinforced plastics (CFRP): an unwanted electro-magnetic wave will be excited under the irradiation of THz waves in conductive molecules and interrupted the reflective waves leading to a blur in the detection results. However, our experimental results showed THz imaging is comparable when it’s used to detect surface defects and shallow ones caused by impact loads. We conducted a set of VT tests as a contrast test, and the VT data are processed by Fourier transform and eigenvector decomposition algorithms, i.e. independent component analysis (ICA). Compared with VT, THz imaging show more details on surface defects and shallow-buried ones due to the absence of thermal diffusion effects. THz imaging could perform non-invasive, non-ionizing and non-contact inspection on the impact damage based on the differences in reflective index. In the end, signal-to-noise (SNR) evaluation is used as an effective means to quantitatively compare the results of these two techniques.

This research develops a method to estimate opaque coating thickness based on time-and-space-resolved thermography. Thermography is a viable technique for measuring coating thickness. However, time-resolved thermography becomes unreliable when uncontrolled constant thermal stimulation amplitude appears. Therefore, a time- and space-resolved thermography technique for coating thickness measurement called restored pseudo heat flux (RPHF) has been developed by using Fourier–Hankel transform. A non-dimensional analysis was conducted and the results show RPHF curves with different coating thicknesses converging as the thermal diffusivity ratio or thermal conductivity ratio goes to one. For large thermal conductivity ratio values, the RPHF curves have two inflection points along the non-dimensional radius. Fifty-nine samples were tested using the proposed method. Support vector regression (SVR) models were constructed with the in-plane distribution of RPHF and the temporal distribution of the measured surface temperature as inputs. To avoid overfitting, cross validation was applied to all the models. Later, another twenty-eight samples were tested to validate the SVR models. The results suggest that a support vector regression model with in-plane profiles of RPHF handles uncontrolled heat flux variation better and yields a better performance in coating thickness measurement than the temporal profile does. With in-plane RPHF profile as input, the SVR model can evaluate coating thickness with a relative root mean square error at 25.3% even when heat amplitude varies 50%.

Anti-reflective coating is widely used on telescopes, eyeglasses and screens to effectively enhance the transmission of light. However, the presence of defects such as bubbles or scratches lowers the usability and functionality of optical film. Optical cameras are often used for coating inspection, but their accuracy relies heavily on the illumination source, camera viewing angles and defect location. This paper describes an active thermography approach that can potentially overcome this issue. Eighteen scratch and bubble defects were located on AR film with dimensions ranging from 0.03mm to 4.4 mm. An infrared camera was used to capture thermal images of those defects over 65 seconds of heating. After the thermal images were acquired, time-domain analysis and space-domain analysis were conducted and k-means clustering methodology was used to highlight the defective area. Results suggest active thermography can be used to detect scratch defects with widths of 0.03mm to 4.40 mm and bubble defects with diameters ranging from 0.08 to 4 mm. For defects with dimensions larger than 0.4 mm, our algorithm can estimate the dimension with less than 15% bias. However, for defects with dimensions less than 0.4mm, the algorithm estimation error ranged from 68% to 900% due to camera resolution limitations. It should be noted that our algorithm can still distinguish a scratch defect with a width of less than one pixel. This study also suggests active thermography can detect scratch and bubble defects regardless of the location of the illumination source.

Effective microwave hyperthermia of cancer is proposed by using slightly lossy flat left-handed material lenses. High-resolution focusing of microwave without any auxiliary foci can be achieved in tissue by jointly adjusting the sources behind the flat lenses. Two-dimensional numerical simulations demonstrate that, for tumor of diameter of 0.6 cm immersed in lossy breast tissue, microwave power can be effectively localized in tumor volume and temperature in tumor may be raised to ∼43.5°C, while keeps below 42°C in the surrounding normal tissue as required in hyperthermia treatment. The proposed Γ-shaped two-lens system is proved to an efficient scheme for microwave hyperthermia. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 1738–1743, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24449

Step heating thermography and modulated frequency thermography are two commonly used inspection methods. This research compares active step heating thermography and modulated frequency thermography for use in detecting defects with diameters of 1.98 mm to 6 mm buried at depths of 1.5 to 4 mm beneath sample surfaces. Experiments were conducted using colored acrylic samples. Step heating thermography can detect shallowly buried defects as early as 4 seconds after heating. Also, lengthening heating time allows step heating thermography to reveal differences between defects of 4 to 6 mm in diameter buried 1.5 mm to 4 mm beneath glass surface. Modulated frequency thermography provides a higher signal-to-noise ratio of 1.3 for smaller defects. Thus it can be used to detect defects with diameters as small as 1.98 mm buried 4 mm beneath the glass surface. However, this level of detection requires phase frequency to be lowered by an order of 10 −3 . An open problem is to develop a systematic method and/or heuristics for determining the appropriate frequency for different defect types.

Optical film provides anti-glare, anti-reflective, and protective features for cell phones and electronic displays such as LCD screens. Due to increased use of optical film, it is challenging for manufacturers to increase their efficiency in producing better quality films. Even a micro scratch in a high-end application of film can lead to a total failure of the display. Optical and visual methods are typically employed to detect defects, but these methods have limitations such as viewing angle artifacts and design of proper illumination source. This paper describes research to utilize artificial neural networks as a non-destructive defect detection model for predicting the presence of pinhole defects in film. An infrared camera captured the thermal response of optical film subjected to heating and cooling. Pinhole defects of various sizes (0.03mm, 0.08mm, 0.2mm, 0.4mm, 0.7mm, 1mm, 2mm, 3mm, 4mm) were investigated. Pinhole defects are one of the most common types of optical film defects. For the process of identification, thermal differences of defective and defect-free regions were investigated. An Artificial Neural Network was trained to use average absolute temperature difference and cooling rate to predict the presence of a defect. The ANN model was trained and verified using separate data sets. The ANN model was able to classify defective and non-defective samples with a 77.8% accuracy rate. The regression coefficient was 0.5874. These results suggest that artificial neural networks can be used for detecting pinhole defects.

Current studies suggest that thermography-based measurements may provide a feasible solution for measuring the thickness of non-metallic coatings. The focus of this research was to build an artificial neural network model to predict coating thickness using active thermography and thickness samples that have not previously been seen by the model. Best results (7.5% error) were achieved when using an ANN model with the derivative of a temperature increment’s real part Laplace transform over the real axis as the input, the gradient descent with momentum back-propagation training algorithm, and 20 hidden nodes.

Graphene is considered as a rising-star material because of its unique properties and it is a promising material for applications in many fields. In recent years, experiments on graphene fabricated by using versatile methods have shed light on the crucial problem of aggregation and restacking, which is induced by strong p–p stacking and van der Waals forces, but preparation methods for real-world applications are still a great challenge. Here we report a facile, rapid, and environmentally friendly process, the burn–quench method, that allows large-scale and controlled synthesis of ordered mesoporous nanographene with 1–5 layers, which has a high surface area and electric conductivity. Electrodes composed of nanographene with a mesoporous architecture used both in electrochemical capacitors and lithium-ion batteries have a high specific capacitance, rate capability, energy density, and cyclic stability. Our results represent an important step toward large-scale graphene synthesis based on this new burn– quench method for applications in high-performance electrochemical energy storage devices.

Microwave heating system for hyperthermia by using four flat LHM lenses orthogonally-deployed around the heating region is proposed and simulated. For the process of heating a 6 mm-diameter tumor in homogeneous lossy dielectric medium, our 2-D FDTD simulations indicate that microwave energy can be concentrated tightly inside the tumor. Also the temperature distribution is derived through bio-heat equation (BHE), which has been solved by finite differential approach. Due to the admiring focusing properties of LHM lens, the temperature inside the tumor may rise from 37degC to more than 42.65degC during one hour with average input power density of 1.60 W/cm at 6 GHz, while the temperature of normal tissue keeps well below 42degC as the requirement of hyperthermia.

The focus-scanning scheme with flat Left-Handed Metamaterial (LHM) lens provides new perspective for near field target detection and imaging. In this paper, concerning practical applications, we further investigate two representative models involving crack detection on the metal surface and the tumor detection in breast tissue. Numerical simulations with 2D FDTD method demonstrate that, when a 0.5 mm-thickness LHM lens being adopted, super-resolution is achieved for a tiny-size crack due to the contribution of evanescent waves; while a tumor cylinder with diameter of 2 mm in breast tissue can be detected by using a 6 cm-thick LHM lens. To improve the longitudinal resolution for the tumor detection, two-orthogonal scanning scheme is employed. Besides, it demonstrates that tissue heterogeneity with random variability of plusmn30% will affects the imaging performance to a limited extent. In the examination on influence factors of imaging resolution, the imaging resolution will become worse as the loss of the medium under detection or the depth of target inside the lossy medium increases.