8.3 Thermografische Verfahren
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Since laser powder bed fusion (PBF-LB/M) is prone to the formation of defects during the building process, a fundamental requirement for widespread application is to find ways to assure safety and reliability of the additively manufactured parts. A possible solution for this problem lies in the usage of in-situ thermographic monitoring for defect detection. In this contribution we investigate possibilities and limitations of the VIS/NIR wavelength range for defect detection. A VIS/NIR camera can be based on conventional silicon-based sensors which typically have much higher spatial and temporal resolution in the same price range but are more limited in the detectable temperature range than infrared sensors designed for longer wavelengths. To investigate the influence, we compared the thermographic signatures during the creation of artificially provoked defects by local parameter variations in test specimens made of a nickel alloy (UNS N07208) for two different wavelength ranges (~980 nm and ~1600 nm).
Many laboratories have been working about Active Thermography
as a Non Destructive Testing method for many
years. This method can be applied on metallic or composites
materials for surface or subsurface defects. Thus, many
different configurations can be encountered to measure the
heat distribution and generate heat flow into the part. Signal
processing is also widely used to improve the performance
of detection.
After encouraging results, aerospace, automotive and energy
industries are now involved into industrialization of the
technology to apply it for production or maintenance applications.
Good practices and common wording are often
required by end-user to qualify the process.
Since the beginning of the 2000s, a working group was
founded within CEN/TC138 'Non-destructive Testing' to define standards in thermography, in the European Committee
for Standardization (CEN). Some other actors have also produced
standards (ISO, IEC, ASTM...).
This paper aims to list the standards currently available about
thermography and the associatd vocabulary. It describes
the generic terms to be used in active and passive thermography
(operating modes, reference blocks, reporting…) and
also more specific elements about laser and induction thermography
for example.
It will also put in perspective the further works to be done
in the next few years to take into account the new trends in
active thermography and how to qualify for industrial applications.
Robotic-assisted 3D scanning and laser thermography for crack inspection on complex components
(2024)
The integration of automation and robotics into inspection processes has marked a transformative shift in the evaluation of complex components. This study presents a novel approach employing robotic-assisted laser thermography for the automated identification and in-depth analysis of cracks in these intricate structures. This method not only streamlines the inspection process but also eliminates the need for numerous manual steps and the use of chemicals associated with traditional methods such as dye penetrant testing. With the increasing com-plexity of components, this is an important step, especially with regard to additively manufactured components, in order to be able to guarantee component safety for a long lifecycle.
Many laboratories have been working about Active Thermography as a Non Destructive Testing method for many years. This method can be applied on metallic or composites materials for surface or subsurface defects. Thus, many different configurations can be encountered to measure the heat distribution and generate heat flow into the part. Signal processing is also widely used to improve the performance of detection. After encouraging results, aerospace, automotive and energy industries are now involved into industrialization of the technology to apply it for production or maintenance applications. Good practices and common wording are often required by end-user to qualify the process. Since the beginning of the 2000s, European Committee for Standardization (CEN) has launched a Working Group within CEN/TC138 to define standards in thermography. Some other actors have also produced standards (ISO, IEC, ASTM,..). This goal of this presentation is to present a status of the standard currently available about thermography and the associated vocabulary. It describes the generic terms to be used in active and passive thermography (operating modes, reference blocks, reporting,…) and also more specific elements about laser and induction thermography for example. It will also put in perspective the further works to be done in the next few years to take into account the new trends in active thermography and how to qualify for industrial applications.
Infrared thermography (IRT) using a focused laser is effective for surface defect detection. Nevertheless, testing complex‐shaped components remains a challenging task. The state‐of‐the‐art focuses on testing a limited region of interest rather than the full sample. Thus, detection and location of surface defects has been less researched. Most attempts require a manual scan of the full sample, which makes it hard to reconstruct the full scanned surface. Here, we introduce a reliable workflow for crack detection and semi‐automated inspection of complex‐shaped components using IRT excited with a laser line. A 6‐axis robot arm is used for moving the sample in front of the setup. This approach has been tested on a section of a rail and a gear, both containing defects due to heavy use. Crack detection is based on the segmentation of thermograms obtained by Fourier transform of sorted temperatures. Moreover, texture mapping is used to visualize a reconstructed thermogram on the 3D model of the sample. Our approach illustrates a reliable process towards the digitalization of thermographic testing.
Additive manufacturing (AM, also known as 3D printing) of metals is becoming increasingly important in industrial applications. Reasons for this include the ability to realize complex component designs and the use of novel materials. This distinguishes AM from conventional manufacturing methods such as subtractive manufacturing (turning, milling, etc.). The most widely used AM process for metals is laser powder bed fusion (PBF-LB/M, also known as selective laser melting SLM). Currently, it has the highest degree of industrialization and the largest number of machines in use. In PBF-LB/M, the feedstock is present as metal powder in an inert gas atmosphere inside a process chamber where a laser melts it locally. By repeatedly lowering the build platform, applying a new layer of powder, and then selectively melting it with the laser, a component is built up layer by layer. The local temperature distributions that occur during this process determine not only the properties of the finished component, but also the possible formation of defects such as pores and cracks. Due to the high relevance of the thermal history for precise geometries and defect formation, a temporally and spatially resolved measurement of quantitative (or real/actual) temperatures would be optimal. Quantitative values would ensure comparability and repeatability of the AM process which would also positively affect the quality and safety of the manufactured component. Furthermore, it would also contribute to the validation of simulations and to a deeper understanding of the manufacturing process itself.
At present, however, only qualitative monitoring of the thermal radiation is performed (e.g., by monitoring the melt pool using a photodiode), and safety-relevant components must be inspected ex situ afterwards which is time-consuming and costly. A reason for the lack of quantitative temperature data from the process are the challenging conditions of the PBF-LB/M process with high scanning speeds and a small laser spot diameter. Furthermore, the emissivity of the surface changes at high dynamics (temporally/spatially) as well as with temperature and wavelength. This specifically makes contactless temperature determination based on emitted infrared radiation challenging for PBF-LB/M. Although classical thermography offers very good qualitative insights, it is not sufficient for a reliable quantitative temperature determination without a complex temperature calibration including image segmentation and assignment of previously determined emissivities.
For this reason, this publication presents the hyperspectral thermography approach for the PBF-LB/M process: The emitted infrared radiation is measured simultaneously at many adjacent wavelengths. In this study, this is realized via a fast hyperspectral line camera that operates in the short-wave infrared range. The thermal radiation of a line on the target is spectrally dispersed and detected to measure the radiant exitance along that line. If the melt pool of the PBF-LB/M process moves through this line at a sufficient frame rate, a spatial reconstruction of an effective melt pool is possible.
One approach to determine the desired emissivities and the quantitative temperature from this hyperspectral data are temperature-emissivity separation (TES) methods. A major problem is that n spectral measurements are available, but n+1 parameters are required for each image pixel (n emissivity values + one temperature value). TES methods offer the possibility to approximate this mathematically underconstrained problem in a reliable and traceable way by analytically parameterizing the spectral emissivity with a few degrees of freedom. Using this approach, setup and method are applied to a research machine for PBF-LB/M, called SAMMIE (Sensor-based Additive Manufacturing Machine). First results under AM process conditions are shown which form the basis for the determination of quantitative temperatures in the PBFLB/M process. This marks an important contribution to improving the comparability and repeatability of production, validating simulations, and understanding the process itself. When fully developed and validated, the presented method can also provide reference measurements to evaluate and optimize other, more practical monitoring methods, such as melt pool monitoring or optical tomography. In the long run, this will help to increase confidence in the safety of AM products.
In this work, we continue to develop and investigate the Thermal Shock Response Spectrum (TSRS) method as an alternative data processing method for infrared thermography (IRT). We focus on improving the current TSRS algorithm and present an optimization methodology for finding the optimal thermal Q-factor and characteristic frequency pair, which is based on the widely applied random sampling method. We show the qualitative relationship between the determined optimal characteristic frequency and the corresponding maximum difference in diffusion length between reference and defective models, as calculated by selecting a specific one-dimensional thermal N-layer model. The investigations were performed on an inhomogeneous plate made of carbon fiber reinforced polymer (CFRP) with artificial square defects at different depths. Furthermore, two different heat sources were used: a xenon flash lamp and a laser. These sources are not only distinct by their underlying physics but also generate inherently different pulse shapes. To quantitatively estimate the contrast between defect and non-defect areas, and to compare these results with commonly used infrared thermography (IRT) data post-processing methods such as Pulse Phase Thermography (PPT) and Thermographic Signal Reconstruction (TSR), the Tanimoto criterion (TC) and signal-tonoise ratio (SNR) were used.
Welded steel structures used in the offshore wind industry are exposed to harsh marine environments, which can result in corrosion-induced fatigue damage. Of particular concern is the heat affected zone (HAZ) of welded joints, a region known for its altered microstructure and mechanical properties, which can significantly influence the initiation and propagation of fatigue cracks. This study investigates the short and long fatigue crack growth rates, and the effect of seawater exposure, for the HAZ in S355 steel weldments. Single-edge notch bend (SENB) specimens are used, with a shallow notch in the HAZ. A series of specimens is immersed in synthetic seawater that is continuously circulated at a controlled temperature to assess the synergistic effects of corrosion and fatigue. The experimental method integrates a novel application of front face strain compliance for monitoring short cracks, alongside an extended back-face strain compliance approach for monitoring long crack propagation. It is concluded that the short fatigue crack growth rate of the HAZ is 2.7 to 3.5 times higher in seawater as compared to air. As the crack propagates and enters into the long crack regime, the ratio decreases to 2.2 times at the transition point of the two-stage crack growth curve and further decreases to 1.5 times when the notch advances towards fracture. The findings indicate that the fatigue crack growth rates documented in standards tend to be on the conservative side. This study significantly enriches the fatigue crack growth data available in literature, which will contribute to a more accurate lifetime assessment offshore wind turbine structures.
Early detection of fatigue cracks and accurate measurements of the crack growth play an important role in the maintenance and repair strategies of steel and composite structures exposed to cyclic loads during their service life. Commonly used non-destructive techniques such as strain gauges, clip gauges, ultrasound, etc. used for detection and monitoring of fatigue damage are contact-based and perform local measurements. In addition, complex full-field techniques are commonly investigated, such as digital image correlation (DIC) and infrared thermography (IRT). In this work, a specific implementation of IRT, called lock-in IRT, is implemented for fatigue damage detection. This technique evaluates the thermal stress response of test specimens, specifically focusing or “locking-in” on the frequency of applied cyclic loads. Three different test scenarios are presented.
First, a section of a wind turbine rotor blade made of a glass fibre reinforced plastic (GFRP) shell structure under cyclic load was examined with Lock-In IRT along with DIC. The primary advantage of Lock-In IRT in this test setup was that it required no sample preparation, as compared to the painting and speckle pattern required for DIC. In the frequency domain, specifically the frequency of applied cyclic load, it was possible to extract local directional inhomogeneous loading within the shell structure due to progressive damage, confirmed with the deformation obtained from DIC results.
Second, thick welded specimens made of structural steel S355 were subjected to multiple NDT methods such as strain gauges, crack luminescence penetration (developed specifically at BAM), ultrasound, and IRT, with the aim of investigating the ability of each technique to detect fatigue damage initiation as early as possible in the total fatigue life of the specimen. Amongst the range of implemented techniques, Lock-In IRT provided the first indication of fatigue crack initiation at the weld toe of the specimens. This was validated with the other techniques as well as fractography.
Third, steel S355 specimens used to manufacture offshore wind turbine monopiles were tested. The specimens were extracted from a plate that was submerged in a marine environment, resulting in a corroded surface with corrosion pits. These specimens were subjected to cyclic tensile loads without removing the corrosion pits. The fatigue tests were monitored using IRT in a special full-field capturing setup that enables both sides of the specimen to be examined with one IRT camera. This allowed the entire pitted surface to be monitored for fatigue damage initiation at the same time. With the implementation of Lock In IRT, the surface stress distribution could be captured (the stress concentration at the pits), and fatigue crack initiation could be detected and linked with specific corrosion pits.
Passive infrared thermography as an inspection tool for operational wind turbine rotor blades
(2024)
The growing wind energy infrastructure presents a significant challenge in the maintenance and operation of wind turbines (WT) and their intricate components. An important aspect of WT maintenance is the inspection of wind turbine rotor blades (WTB) to ensure the overall health and safety of the turbine. This inspection process involves both visual and mechanical examinations of the blades to identify any indicators of damage or wear that could compromise their performance and, consequently, the structural integrity of the entire WT system. The complexity of WTBs is compounded by their ever-expanding dimensions, exceeding 100 meters in length for 16 MW WT systems, and their multi-material composition. Within this context, passive infrared thermography emerges as a potential alternative to conventional contact- or proximity-based inspection methods. Unlike active thermography, passive thermography uses solar radiation and ambient temperature variation for thermal contrast, eliminating the need for traditional heat lamps, flash, or laser-based techniques.
A novel inspection method has been developed to semi-autonomously assess wind turbine blades (WTBs) while the wind turbine (WT) is operational, from ground level. This approach leverages optimal thermal contrast, which depends on prevailing weather conditions during field measurements, enabling the visualization of both external and internal features of the WTBs through post-processing techniques. In this study, thermal data obtained through passive thermography is compared with contemporaneous visual imagery to definitively classify observed features in thermal images as either surface or sub-surface features. This analysis, coupled with corresponding weather conditions, provides valuable insights into the capabilities and limitations of the inspection technique. Additionally, finite-element-based (FE) thermal simulations of a WTB section are employed to parametrically assess the influence of weather conditions, beyond those observed during field measurements, based on a validated model.
In addition, the thermal images also consist of thermal signatures of leading-edge turbulence due to possible leading-edge erosion in WTBs. These are primarily vortices, and their shape and size depend on the morphology of the damage as well as the rotational speed of the WTBs. The inspections are accompanied by automatic data evaluation of the thermal signatures. To improve the precision of erosion damage identification, a fully convolutional network (FCN) is employed, trained, and tested using over 1000 annotated thermographic blade images. Additionally, the study introduces strategies for grouping smaller damage indications and simplification rules based on realistic thermal imaging resolutions. As leading-edge erosion could potentially lead to annual energy production (AEP) losses, this technique could prove to be a powerful tool in establishing the presence of damage and the resulting AEP loss.