Showing papers on "Thermography published in 2020"

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Abstract: Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented.

Generative Principal Component Thermography for Enhanced Defect Detection and Analysis

Abstract: Machine learning methods play an important role in the nondestructive testing field for quality assessment of polymer composites. As a popular deep learning branch, a generative adversarial network is introduced to the thermography field as an image augmentation approach to improve its defect detection performance. Specifically, a generative principal component thermography (GPCT) method for defect detection in polymer composites is proposed. By employing the data augmentation strategy, more informative images are generated to enlarge the diversity of the original set of images. The defect detection results can be visualized using a number of interpretable features. Consequently, the defect detection performance of thermographic data analysis can be enhanced to some extent. The experimental results on a carbon fiber reinforced polymer specimen demonstrate the feasibility and advantages of the GPCT method.

Development and Application of Infrared Thermography Non-Destructive Testing Techniques.

Abstract: Effective testing of defects in various materials is an important guarantee to ensure its safety performance. Compared with traditional non-destructive testing (NDT) methods, infrared thermography is a new NDT technique which has developed rapidly in recent years. Its core technologies include thermal excitation and infrared image processing. In this paper, several main infrared thermography nondestructive testing techniques are reviewed. Through the analysis and comparison of the detection principle, technical characteristics and data processing methods of these testing methods, the development of the infrared thermography nondestructive testing technique is presented. Moreover, the application and development trend are summarized.

The Feature Recognition of CFRP Subsurface Defects Using Low-Energy Chirp-Pulsed Radar Thermography

Abstract: In this article, a low-energy chirp-pulsed radar thermography (CP-RT) is used to detect the subsurface delamination of carbon fiber reinforced polymer (CFRP) composite as nondestructive testing and evaluation (NDT&E) techniques. The CFRP specimen with artificial flat-bottom holes (FBHs) is prepared for NDTandE by chirp-pulsed radar thermography. Two lasers are employed to be external excitation heat sources. The laser intensities are modulated according to a chirp-pulsed radar signal that combines linear frequency modulation and pulse excitation, and the temperature rise is controlled within 2 °C in the experiment. The thermal-wave response signal is processed by a series of different postprocessing characteristic extraction algorithms. These algorithms include time–frequency algorithms [crosscorrelation algorithm (CC), fast Fourier transform (FFT), and dual-orthogonal demodulation algorithm (DOD)] and statistical analysis approaches [principal component analysis (PCA) and PCA-based reconstructed independent component analysis (PCA-RICA)]. The signal-to-noise ratio (SNR) of defects is employed to evaluate the defect detectability for different size defects by different postprocessing algorithms. A three-dimensional (3-D) tomography method based on the FFT phase characteristic is proposed. A truncated-correlation photothermal tomography based on DOD is also introduced to enable the 3-D tomography of CFRP specimen. The FFT phase presents a relatively high SNR and has good correlation with the depth of the defect. The FFT-based CP-RT has the potential to provide a rapid NDT&E and 3-D tomography approach for CFRP with subsurface defects under the low-energy excitation condition.

Natural Crack Diagnosis System Based on Novel L-Shaped Electromagnetic Sensing Thermography

Abstract: Nondestructive detection of small fatigue cracks is a critical and challenging task in evaluating the properties of material. This article proposes a novel L-shaped ferrite magnetic open sensing structure of the eddy current pulsed thermography system for fatigue crack inspection on metallic materials with anomalistic geometry. The theoretical derivation model of the proposed structure is developed to generate a guided distribution of electromagnetic field for enhancing the weak thermal signal detection. The proposed detection model provides a region of interest that has relatively uniform magnetic field. This significantly enhances the detectability and thermal contrast of omnidirectional microfatigue cracks. In addition, the detection is completely in the open view of the infrared camera, and the configuration has advantages of dramatically increasing portability and efficiency for detecting complex workpiece. Experiments on natural cracks in several samples have been conducted to validate the reliability and efficiency of the proposed system.

Two-stream convolutional neural network for non-destructive subsurface defect detection via similarity comparison of lock-in thermography signals

Abstract: Active infrared thermography is a safe, fast, and low-cost solution for subsurface defects inspection, providing quality control in many industrial production tasks. In this paper, we explore deep learning-based approaches to analyze lock-in thermography image sequences for non-destructive testing and evaluation (NDT&E) of subsurface defects. Different from most existing Convolutional Neural Network (CNN) models that directly classify individual regions/pixels as defective and non-defective ones, we present a novel two-stream CNN architecture to extract/compare features in a pair of 1D thermal signal sequences for accurate classification/differentiation of defective and non-defective regions. In this manner, we can significantly increase the size of the training data by pairing two individually captured 1D thermal signals, thereby greatly easing the requirement for collecting a large number of thermal sequences of specimens with defects to train deep CNN models. Moreover, we experimentally investigate a number of network alternatives, identifying the optimal fusion scheme/stage for differentiating the thermal behaviors of defective and non-defective regions. Experimental results demonstrate that our proposed method, directly learning how to construct feature representations from a large number of real-captured thermal signal pairs, outperforms the well-established lock-in thermography data processing techniques on specimens made of different materials and at various excitation frequencies.

UAV-based thermal imaging in the assessment of water status of soybean plants

Abstract: Soybean production both in Brazil and globally has regularly been threatened by drought periods. The use of infrared thermography to evaluate the canopy’s temperature and its relationship with plan...

Joint Scanning Laser Thermography Defect Detection Method for Carbon Fiber Reinforced Polymer

Abstract: 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.

FLIR vs SEEK thermal cameras in biomedicine: comparative diagnosis through infrared thermography

Abstract: In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study, present the current utility of thermal camera models namely FLIR and SEEK in biomedical applications as an extension of our previous article. The most significant result is the differences between image qualities of the thermograms captured by thermal camera models. In other words, the image quality of the thermal images in FLIR One is higher than SEEK Compact PRO. However, the thermal images of FLIR One are noisier than SEEK Compact PRO since the thermal resolution of FLIR One is 160 × 120 while it is 320 × 240 in SEEK Compact PRO. Detecting and revealing the inhomogeneous temperature distribution on the injured toe of the subject, we, in this paper, analyzed the imaging results of two different smartphone-based thermal camera models by making comparison among various thermograms. Utilizing the feasibility of the proposed method for faster and comparative diagnosis in biomedical problems is the main contribution of this study.

Quantitative Evaluation of Crack Depths on Thin Aluminum Plate Using Eddy Current Pulse-Compression Thermography

Abstract: Eddy current (EC) stimulated thermography is an emerging technique for nondestructive testing and evaluation of conductive materials. However, quantitative estimation of the depth of subsurface defects in metallic materials by thermography techniques remains challenging due to significant lateral thermal diffusion. This article presents the application of eddy current (EC) pulse-compression thermography to detect surface and subsurface defects with various depths in an aluminum (AL) sample. Kernel principal component analysis and low rank sparse modeling were used to enhance the defective area, and cross-point feature was exploited to quantitatively evaluate the defects’ depth. Based on experimental results, it is shown that the crossing point feature has a monotonic relationship with surface and subsurface defects’ depth, and it can also indicate whether the defect is within or beyond the EC skin depth. In addition, the comparison study between AL and composites in terms of impulse response and proposed features are also presented.

The influence of ambient environmental conditions in detecting bridge concrete deck delamination using infrared thermography (IRT)

Abstract: Delamination is a serious form of deterioration in concrete bridge decks. Infrared thermography (IRT) is an advance non‐destructive testing method for concrete bridge deck delamination detection by capturing the absolute thermal contrast (ΔT) on the concrete surface caused by the disruption in heat flow due to subsurface defects. However, as the ambient environmental conditions (e.g. wind velocity and solar radiation) of a bridge could significantly affect the measurement outcomes of IRT, the optimal times for infrared data collection are still unclear. In this paper, a series of experimental and numerical studies were carried out to investigate the effects of the rate of heat flux and wind velocity on ΔT on the surface of bridge decks with the aim of identifying the optimal inspection times for different geometry characteristics of delamination (i.e. size and depth). The developed model is firstly validated by the experimental data and then a series of parametric studies were carried out. The result shows that the heat flux rate plays an important role in the development of ΔT on concrete surface, especially for a relatively shallow and small size delamination. However, the influence of heat flux rate gradually diminishes with the increase in size and depth of delamination. In addition, it demonstrates that there is a positive linear correlation between the total heat energy (external irradiation) and square of the delamination depth. The current research represents an important step towards the development of an effective and efficient way for defect detection using IRT.

Improved impact damage characterisation in CFRP samples using the fusion of optical lock-in thermography and optical square-pulse shearography images

Abstract: Image fusion methods with optical lock-in thermography (OLT) and optical square-pulse shearography (OSS) images are proposed to characterise impact damages in carbon fibre reinforced plastic (CFRP) plates. The samples were damaged with low-energy impacts and inspected using OLT, OSS and the reference ultrasound (US) time-of-flight C-scans. A total of 1113 combinations of decomposition, preprocessing, segmentation and fusion tools were proposed and compared with OLT, US and OSS results using the equivalent diameter criterion and the Matthews’ correlation coefficient. The results indicated a reduction of 72.21% in the equivalent diameter measurement error and a metric enhancement of 8.05% when using the fusion, showing that one of the developed image fusion methods can successfully perform improved impact damage inspections.

Adaptive spectral band integration in flash thermography : enhanced defect detectability and quantification in composites

Abstract: In flash thermography, the maximum inspectable defect depth is limited when only the raw thermographic sequence is analyzed. The introduction of pulsed phase thermography (PPT), in which phase (contrast) images at different thermal wave frequencies are obtained, significantly improved the maximum inspectable depth while reducing the effects of non-uniform heating and non-uniform surface properties. However, in a practical environment, the evaluation of many phase images per inspection is a cumbersome procedure. In this paper, a novel Adaptive Spectral Band Integration (ASBI) procedure is introduced for the post-processing of flash thermographic datasets, which yields a unique damage index map. ASBI integrates the most useful spectral information for each pixel individually, obtaining a maximized defect detectability and an almost zero-reference level. The performance of ASBI with respect to defect detectability as well as defect sizing and depth inversion is evaluated thoroughly with both experimentally and numerically generated datasets. The ASBI procedure is successfully applied on various composite coupons with flat bottom holes and barely visible impact damage, as well as on a stiffened aircraft composite panel with a complex cluster of production defects. The ASBI procedure is compared with existing data-processing techniques in literature, illustrating an enhanced performance.

Nondestructive evaluation of thermal barrier coating thickness degradation using pulsed IR thermography and THz-TDS measurements: A comparative study

Abstract: Thermal barrier coatings are extensively used in aircraft engines. During service, the TBC coatings degrade because of erosion and sintering by hot gas flow and also by localized wear due to rubbing of flaps with spacers. It is necessary to assess the condition of the coatings as a function of service life through suitable non-destructive means. Pulse Thermography (PT) and Terahertz-Time Domain Spectroscopy (THz-TDS) techniques are used to evaluate the degree of degradation of the thin Air Plasma Sprayed (APS) TBCs top coat thickness. Infrared thermography has the advantage of fast inspection of a large area. In this work, we used a simplified analytical model aided calibration and development of a regression model to quantitatively analyze the thickness degradation in real-world TBC samples that have endured varying service life. These measurements were later verified using THz-TDS imaging, an emerging technique for accurate thickness measurements. Assuming the refractive index of the topcoat material, Yttria-Stabilized Zirconia (YSZ) as 4.8, the topcoat thickness of the entire specimen has been estimated using THz-TDS reflection mode setup. Results show that, the thickness values are varying between 94.94 μ m - 114.96 μ m for 500 h serviced samples and 32.5 μ m - 91.96 μ m in the case of 1000 h serviced samples demonstrating loss of TBC with increased service life. Comparison of the pulse thermography results with THz-TDS reveals a mean relative error of less than 10.3 % in TBC thickness estimation. Further the results of both the techniques are cross-validated with Eddy current testing and optical microscopy. The proposed non-destructive techniques for TBC estimation will aid in the accurate creation of engine digital twin and will help in scheduling preventive maintenance measures thus increasing the life and safety of key aircraft engine components.

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Abstract: Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRUs) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

Experimental Determination of the Emissivity of Powder Layers and Bulk Material in Laser Powder Bed Fusion Using Infrared Thermography and Thermocouples

Abstract: Recording the temperature distribution of the layer under construction during laser powder bed fusion (L-PBF) is of utmost interest for a deep process understanding as well as for quality assurance and in situ monitoring means. While having a notable number of thermal monitoring approaches in additive manufacturing (AM), attempts at temperature calibration and emissivity determination are relatively rare. This study aims for the experimental temperature adjustment of an off-axis infrared (IR) thermography setup used for in situ thermal data acquisition in L-PBF processes. The temperature adjustment was conducted by means of the so-called contact method using thermocouples at two different surface conditions and two different materials: AISI 316L L-PBF bulk surface, AISI 316L powder surface, and IN718 powder surface. The apparent emissivity values for the particular setup were determined. For the first time, also corrected, closer to real emissivity values of the bulk or powder surface condition are published. In the temperature region from approximately 150 °C to 580 °C, the corrected emissivity was determined in a range from 0.2 to 0.25 for a 316L L-PBF bulk surface, in a range from 0.37 to 0.45 for 316L powder layer, and in a range from 0.37 to 0.4 for IN718 powder layer.

Evaporation of Initially Heated Sessile Droplets and the Resultant Dried Colloidal Deposits on Substrates Held at Ambient Temperature.

Abstract: The present study experimentally and numerically investigates the evaporation and resultant patterns of dried deposits of aqueous colloidal sessile droplets when the droplets are initially elevated to a high temperature before being placed on a substrate held at ambient temperature. The system is then released for natural evaporation without applying any external perturbation. Infrared thermography and optical profilometry are used as essential tools for interfacial temperature measurements and quantification of coffee-ring dimensions, respectively. Initially, a significant temperature gradient exists along the liquid-gas interface as soon as the droplet is deposited on the substrate, which triggers a Marangoni stress-induced recirculation flow directed from the top of the droplet toward the contact line along the liquid-gas interface. Thus, the flow is in the reverse direction to that seen in the conventional substrate heating case. Interestingly, this temperature gradient decays rapidly within the first 10% of the total evaporation time and the droplet-substrate system reaches thermal equilibrium with ambient thereafter. Despite the fast decay of the temperature gradient, the coffee-ring dimensions significantly diminish, leading to an inner deposit. A reduction of 50-70% in the coffee-ring dimensions is recorded by elevating the initial droplet temperature from 25 to 75 °C for suspended particle concentration varying between 0.05 and 1.0% v/v. This suppression of the coffee-ring effect is attributed to the fact that the initial Marangoni stress-induced recirculation flow continues until the last stage of evaporation, even after the interfacial temperature gradient vanishes. This is essentially a consequence of liquid inertia. Finally, a finite-element-based two-dimensional modeling in axisymmetric geometry is found to capture the measurements with reasonable fidelity and the hypothesis considered in the present study corroborates well with a first approximation qualitative scaling analysis. Overall, together with a new experimental condition, the present investigation discloses a distinct nature of Marangoni stress-induced flow in a drying droplet and its role in influencing the associated colloidal deposits, which was not explored previously. The insights gained from this study are useful to advance technical applications such as spray cooling, inkjet printing, bioassays, etc.

Principal Component Thermography for Defect Detection in Concrete.

Abstract: The goal of the condition assessment of concrete structures is to gain an insight into current condition of concrete and the existence of defects, which decrease durability and usability of the structure. Defects are quite difficult to detect using infrared thermography when concrete elements cannot be thermally excited with the Sun, together with unfavorable thermophysical properties of concrete structures. In this paper, principal component thermography (PCT) is applied as a post-processing method to a sequence of thermograms in order to enhance defect detectability in concrete structures. Defects are detected by analyzing a set of empirical orthogonal functions (EOFs), which were acquired by applying principal component analysis to a sequence of thermograms. The research was performed using concrete samples containing known defects, which were tested using a step heating thermography setup. The results of presented research show that PCT is an effective post-processing method to improve defect detection in concrete structures. By effectively improving the defect detection, PCT has a potential to improve the non-destructive testing (NDT) accuracy of using infrared thermography (IRT) on concrete structures, especially in shaded areas of such structures. The research also shows the defect detectability depending on concrete type thermal excitation setup and defect geometry.

Millikelvin-resolved ambient thermography

Abstract: Thermography detects surface temperature and subsurface thermal activity of an object based on the Stefan-Boltzmann law. Impacts of the technology would be more far-reaching with finer thermal sensitivity, called noise-equivalent differential temperature (NEDT). Existing efforts to advance NEDT are all focused on improving registration of radiation signals with better cameras, driving the number close to the end of the roadmap at 20 to 40 mK. In this work, we take a distinct approach of sensitizing surface radiation against minute temperature variation of the object. The emissivity of the thermal imaging sensitizer (TIS) rises abruptly at a preprogrammed temperature, driven by a metal-insulator transition in cooperation with photonic resonance in the structure. The NEDT is refined by over 15 times with the TIS to achieve single-digit millikelvin resolution near room temperature, empowering ambient thermography for a broad range of applications such as in operando electronics analysis and early cancer screening.

Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

Abstract: Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

Simulation and Experimental Investigation on Active Thermography Test of the Solder Balls

Abstract: The miniaturized electronic products require not only high density, but also high reliability. Defect inspection for the high-density package becomes challenging gradually. In this article, active thermography technology is used to detect the solder balls in flip-chip (FC) packages. A single-layer FC model is constructed and heat conduction in the FC package is simulated by using the finite-element analysis code of COMSOL. Experimental investigation is also carried out to inspect the solder defects by using the laser excitation thermography test system. Infrared images of the SFA1 package are captured, and the spatial adaptive filtering algorithm is used to remove the thermal noise. The hot spots of the solder balls are segmented from the filtered infrared image, and the representative features are extracted for each hot spot. The modified learning vector quantization (LVQ) neural network is used for the classification of the solder balls. The missing solder balls are identified accurately. The simulation and experimental results prove that the proposed approach using active thermography and LVQ algorithm is effective for defect inspection in high-density electronic devices.