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Additively manufactured (AM) metallic sheet-based Triply Periodic Minimal Surface Structures (TPMSS) meet several requirements in both bio-medical and engineering fields: Tunable mechanical properties, low sensitivity to manufacturing defects, mechanical stability, and high energy absorption. However, they also present some challenges related to quality control. In fact, the optimization of both the AM process and the properties of TPMSS is impossible without considering structural characteristics as manufacturing accuracy, internal defects, and as well as surface topography and roughness. In this study, the quantitative non-destructive analysis of TPMSS manufactured from Ti-6Al-4V alloy by electron beam melting was performed by means of laboratory X-ray computed tomography (XCT).
Thermographic non-destructive testing is based on the interaction of thermal waves with inhomogeneities. The propagation of thermal waves from the heat source to the inhomogeneity and to the detection surface according to the thermal diffusion equation leads to the fact that two closely spaced defects can be incorrectly detected as one defect in the measured thermogram. In order to break this spatial resolution limit (super resolution), the combination of spatially structured heating and numerical methods of compressed sensing can be used. The improvement of the spatial resolution for defect detection then depends in the classical sense directly on the number of measurements. Current practical implementations of this super resolution detection still suffer from long measurement times, since not only the achievable resolution depends on performing multiple measurements, but due to the use of single spot laser sources or laser arrays with low pixel count, also the scanning process itself is quite slow. With the application of most recent high-power digital micromirror device (DMD) based laser projector technology this issue can now be overcome.
Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. The growing diversity of construction and demolition waste is leading to increasing difficulties in separating the individual materials. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions.
A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) and visual (VIS)/ near-infrared (NIR) spectroscopy. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.). Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic/technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.
First LIBS measurements show promising results in distinguishing various material types. A meaningful validation shall be achieved with further practical samples. Future works will investigate the combination of LIBS and VIS/NIR spectroscopy in a fully automated measurement setup with conveyor belt speeds of 3 m/s.
The finite volume method (FVM), like the finite element method (FEM), is a numerical method for determining an approximate solution for partial differential equations. The derivation of the two methods is based on very different considerations, as they have historically evolved from two distinct engineering disciplines, namely solid mechanics and fluid mechanics. This makes FVM difficult to learn for someone familiar with FEM. In this paper we want to show that a slight modification of the FEM procedure leads to an alternative derivation of the FVM. Both numerical methods are starting from the same strong formulation of the problem represented by differential equations, which are only satisfied by their exact solution. For an approximation of the exact solution, the strong formulation must be converted to a so-called weak form. From here on, the two numerical methods differ. By appropriate choice of the trial function and the test function, we can obtain different numerical methods for solving the weak formulation of the problem. While typically in FEM the basis functions of the trial function and test function are identical, in FVM they are chosen differently. In this paper, we show which trial and test function must be chosen to derive the FVM alternatively: The trial function of the FVM is a “shifted” trial function of the FEM, where the nodal points are now located in the middle of an integration interval rather than at the ends. Moreover, the basis functions of the test function are no longer the same as those of the trial function as in the FEM, but are shown to be a constant equal to 1. This is demonstrated by the example of a 1D Poisson equation.
Sound production due to main-flow oriented vorticity-nozzle interaction in absence of a net swirl
(2022)
The downstream acoustic response due to the interaction of main-flow oriented vorticity with a choked nozzle in a swirl-free flow was experimentally demonstrated. The response was obtained by means of impulsive radial air injection in the pipe upstream from the nozzle. The resulting downstream acoustic data are shown to obey a scaling rule that differs, from the one for swirl-nozzle interaction, which according to the literature is proportional to the square of the swirl number. In contrast, here evidence is presented that points to the scaling of main-flow oriented vorticity noise with the cross-sectional average of the square of the transversal velocity at the throat divided by the square of the critical sound speed.
In this study, the concept of a Zero Mass Flow Liner is evaluated. The concept enables impedance control by the induction of periodic bias flow through the perforated facing sheet of the liner. The periodic bias flow is generated by a secondary high amplitude acoustic actuation. By means of the periodic bias flow, the liner can be tuned to different operating points in a given range of grazing flow velocities. The equivalent fluid impedance model for perforated plates is modified to account for the effects of periodic bias flow and grazing flow. An optimization routine, based on a genetic algorithm, is implemented. The method is applicable to any liner concept and uses the impedance of the lined surface as boundary condition in a numerical simulation. Thereby, a set of liner parameters is derived in order to obtain the desired damping characteristics. Based on the results of the optimization, a Zero Mass Flow Liner is manufactured and consequently evaluated experimentally. The damping characteristics are evaluated in form of the dissipated energy along the lined surface. Prediction and measurements show agreement. The Zero Mass
Flow Liner delivers broad band dissipation of high peak value over a range of grazing flow Mach numbers. Under grazing ow conditions, the effect of periodic bias flow is reduced. This poses high energy requirements in high Mach number flow regimes which might restrict the applicability of the Zero Mass Flow concept to grazing flows of low Mach numbers.
The residual stresses and load transfer in multiphase metal alloys and their composites (with both random planar-oriented short fibers and particles) will be shown, as studied by neutron diffraction, by X-ray computed tomography, and by a model based on the reformulation of classic Maxwell’s homogenization method.
Contrary to common understanding and state-of-the-art models, we experimentally observe that randomly oriented phases possess non-hydrostatic residual stress. Moreover, we disclose that the unreinforced matrix alloy stays under hydrostatic compression even under external uniaxial compression.
The recently developed modeling approach allows calculating the residual stress in all phases of the composites. It rationalizes the presence of deviatoric stresses accounting for the interaction of random oriented phases with fibers having preferential orientation. It also allows the explanation of the unconventional in-situ behavior of the unreinforced alloy and the prediction of the micromechanical behavior of other similar alloys.
A large and increasing number of scientific domains pushes for high neutron imaging resolution achieved in reasonable times. Here we present the principle, design and performance of a detector based on infinity corrected optics combined with a crystalline Gd3Ga5O12 : Eu scintillator, which provides an isotropic sub-4 μm true resolution. The exposure times are only of a few minutes per image. This is made possible also by the uniquely intense cold neutron flux available at the imaging beamline NeXT-Grenoble. These comparatively rapid acquisitions are compatible with multiple high quality tomographic acquisitions, opening new venues for in-operando testing, as briefly exemplified here.
Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed.
In this work, we present the recent in-situ imaging developments at the BAMline (of synchrotron BESSY II, HZB), focused on the in-situ characterization and understanding of microstructural evolution of additively manufactured materials subjected to different environments. Two show cases are presented. In the first, X-ray refraction radiography (SXRR) was combined with in-situ heat treatment to monitor the microstructural evolution as a function of temperature in a laser powder bed fusion (LPBF) manufactured AlSi10Mg alloy. We show that SXRR allows detecting the changes in the Si-phase morphology upon heating using statistically relevant volumes. SXRR also allows observing the growth of pores (i.e., thermally induced porosity), usually studied via X-ray computed tomography (XCT), but using much smaller fields-of-view. In the second case study, XCT was combined with in-situ tensile test to investigate the damage mechanism in a LPBF Aluminum Metal Matrix Composite (MMC). In-situ SXCT test disclosed the critical role of the defects in the failure mechanism along with pre-cracks in the reinforcement phase of MMC. We found that cracks were initiated from lack-of-fusion defects and propagated through coalescence with other defects.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
An experimental cold-gas study of the response of a choked convergent–divergent nozzle to swirl perturbations is presented. The perturbations were obtained by means of upstream unsteady tangential injections into initially steady flows with different values of steady background swirl. The swirl perturbations induced changes in the axial mass-flow rate, due to either their ingestion or evacuation by the nozzle. This in turn caused a downstream acoustic response. For low-intensity background swirl the responses were found to be similar to those obtained without steady background swirl. Perturbations of a high-intensity background swirl led to different effects. For long injection times, the negative mass-flow rate modulation occurred in two stages. The first stage was similar to that of the background-swirl free case. The second stage occurred after a short time delay, and induced a much stronger negative acoustic response. This unexpected behavior suggests that a significant part of the tangentially injected fluid flows upstream inducing an accumulation of swirl, which is – after tangential injection is ceased – suddenly cleared out through the nozzle. A scaling rule for the amplitudes of these acoustic responses is reported. Furthermore, quasi-steady models, based on steady-state measurements are proposed. These models predict the downstream acoustic response amplitude within a factor two. Additionally, preliminary empirical evidence of the effect of swirl on the downstream acoustic response due to the interaction of entropy patches with a choked nozzle is reported. This was obtained by comparison of sound produced by abrupt radial or tangential sonic injection, upstream from the choked nozzle, of air from a reservoir at room temperature to that from a reservoir with a higher stagnation temperature. Because the mass flow through the nozzle does not increase instantaneously, the injected higher-enthalpy air accumulates upstream of the injection-port position in the main flow. This eventually induces a large downstream acoustic pulse when tangential injection is interrupted. The magnitude of the resulting sound pulse can reach that of a quasi-steady response of the nozzle to a large air patch with a uniform stagnation temperature equal to that of the upstream-injected heated air. This hypothesis is consistent with the fact that the initial indirect-sound pulse is identical to one obtained with unheated air injection. The authors posit that – given all of the insight gleaned from them in this case – acoustic measurements of indirect sound appear to be a potentially useful diagnostic tool.
Background Due to physical coupling between mechanical stress and magnetization in ferromagnetic materials, it is assumed in the literature that the distribution of the magnetic stray field corresponds to the internal (residual) stress of the specimen.
The correlation is, however, not trivial, since the magnetic stray field is also influenced by the microstructure and the geometry of component. The understanding of the correlation between residual stress and magnetic stray field could help to evaluate the integrity of welded components.
Objective This study aims at understanding the possible correlation of subsurface and bulk residual stress with magnetic stray field in a low carbon steel weld. Methods The residual stress was determined by synchrotron X-ray diffraction (SXRD, subsurface region) and by neutron diffraction (ND, bulk region). SXRD possesses a higher spatial resolution than ND. Magnetic stray fields were mapped by utilizing high-spatial-resolution giant magneto resistance (GMR) sensors.
Results The subsurface residual stress overall correlates better with the magnetic stray field distribution than the bulk stress.
This correlation is especially visible in the regions outside the heat affected zone, where the influence of the microstructural features is less pronounced but steep residual stress gradients are present.
Conclusions It was demonstrated that the localized stray field sources without any obvious microstructural variations are associated with steep stress gradients. The good correlation between subsurface residual stress and magnetic signal indicates that the source of the magnetic stray fields is to be found in the range of the penetration depth of the SXRD measurements.
The dataset presented contains ultrasonic data recorded in pulse echo mode. The investigated specimen is made of the isotropic homogeneous material polyamide and has a drill hole of constant diameter running parallel to the surface, which was scanned in a point grid using an automatic scanner system. At each measuring position, a pitch-catch measurement was performed using a sampling rate of 2 MHz. The probes used are arrays consisting of a spatially separated receiving and in-phase transmitting unit. The transmitting and receiving sides each consist of 12 point-shaped single probes. These dry-point contact (DPC) probes operate according to the piezoelectric principle at nominal frequencies of 55 kHz (shear waves) and 100 kHz (longitudinal waves), respectively, and do not require a coupling medium. The measurements are performed with longitudinal (100 kHz) and transverse (55 kHz) waves with different geometric orientations of the probe on the measurement surface. The data presented in the article provide a valid source for evaluating reconstruction algorithms for imaging in the low-frequency ultrasound range.
Atmospheric pressure plasmas interact in various physical ways with their surroundings. They release heat and generate charge carriers, which leads to two effects. The first effect is the generation of acoustic waves due to heat release, known as the thermoacoustic effect, and the second effect is the perturbation of the resting fluid provoked by the release of charge carriers, called “ionic wind”. The direct connection between the charge carrier production of the discharge arrangement and the surroundings also allows the detection of acoustic waves by tracking the electrical current of the arrangement.
This contribution introduces a multi-fluid model approach to describe the acoustic interaction of atmospheric plasmas. In addition, we present experimental results on commercially available and in-house fabricated discharge arrangements for either emitting or receiving acoustic waves.
This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
This paper describes a new ultrasonic measuring device called “W-Box”. It was developed based on the requirements of the DFG Forschergruppe (research unit) CoDA for a portable device for monitoring of concrete specimens, models and actual structures using embedded ultrasonic transducers as well as temperature and humidity sensors. The W-Box can send ultrasonic pulses with a variable frequency of 50–100 kHz to one selectable transducer and records signals from up to 75 multiplexed channels with a sample rate of 1 MHz and a resolution of 14 bits. In addition, it measures temperature and humidity with high accuracy, adjustable amplification, restarts automatically after a power failure and can be fully controlled remotely. The measured data are automatically stored locally on-site data quality checks and transferred to remote servers. The comparison of the W-Box with a laboratory setup using commercial devices proves that it is equally reliable and precise, at much lower cost. The W-Box also shows that their measurement capacities, with the used embedded ultrasonic transducers, can reach above 6 m in concrete.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.
Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed.
Additively manufactured (and in particular laser powder bed fused) materials represent a manyfold challenge for the materials scientist and engineer because of their distinctive microstructure. If laser powder bed fusion is used to produce components, the complexity level increases because meso-structures (e.g., overhanging features, surface and internal defects) gain importance. Furthermore, if the main advantage of additive manufacturing, i.e., the freedom of design, is to be fully exploited, and geometrically complex structures, such as lattices, are manufactured, then such structures become meta-materials. This means that the geometry and the materials properties become equally important.
This matryoshka-like (more literary than the dry “multi-scale”) complexity makes the characterization of the residual stress fields by means of diffraction methods so difficult with the current means, that new paradigms are necessary to tackle the challenge.
Indeed, classic open problems acquire an extra layer of difficulty, such that new solutions need to be found and the sometimes-dormant debate needs to be re-opened. Examples include the determination of: a- the unstrained reference: this reference can become location-dependent and needs to be carefully determined; b- the so-called diffraction elastic constants, which becomes immensely challenging since even the single-crystal elastic constants are not known for additively manufactured materials.
On top of this, other problems arise. The determination of the principal axes of stress becomes non-trivial because the hatching strategy sometimes dominates over the sample geometry. Even further, in complex structures, such as lattices, the textbook statement that the strain measurement in six independent directions uniquely identifies the strain tensor becomes simply invalid. The peculiar surface features of additively manufactured materials transform trivial tasks into formidable challenges: the precise alignment of a specimen in a beam or the determination of surface stresses with laboratory X-rays need to be thoroughly re-discussed and lay far from being routine tasks.
In this paper, we will show a few examples of the cases mentioned above. We will demonstrate that sometimes the classic approach works very well, but other times surprising conclusions can be drawn from in-depth studies of the residual stress in additively manufactured materials. In short, we predicate that classic methods cannot be used on additively manufactured materials and structures without a critical evaluation of their validity and application range.
Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network.
[1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022)
https://dx.doi.org/10.1016/j.imatprotec.2021.117473
[2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013)
https://dx.doi.org/10.1007/978-3-642-40669-0_33
Atmospheric pressure plasmas interact in various physical ways with their surroundings. They release heat and generate charge carriers, which leads to two effects. The first effect is the generation of acoustic waves due to heat release, known as the thermoacoustic effect, and the second effect is the perturbation of the resting fluid provoked by the release of charge carriers, called “ionic wind”. The direct connection between the charge carrier production of the discharge arrangement and the surroundings also allows the detection of acoustic waves by tracking the electrical current of the arrangement.
This contribution introduces a multi-fluid model approach to describe the acoustic interaction of atmospheric plasmas. In addition, we present experimental results on commercially available and in-house fabricated discharge arrangements for either emitting or receiving acoustic waves.
This contribution presents a set of largely novel reference standards specially designed for testing different important physical effects and functionalities of radiography-based computed tomography (CT) simulation software. These standards were developed within the scope of the German cooperation project “CTSimU – Radiographic Computed Tomography Simulation for Measurement Uncertainty Evaluation” [1] and serve as tools for the basic qualification of the sufficient physical correctness and required features of simulation software of CT-based coordinate measurement systems (CMSs) via the analyses either of 2D projection images only or of full CT scans. The results serve as input to the German standardisation committee for the development of a new national VDI/VDE guideline in the series VDI/VDE 2630 dealing with the basic qualification aspect of CT simulation software and shall lay ground for the measurement uncertainty determination of dimensional measurements using CT.
An important focus of research in Industrial X-ray Computed Tomography (CT) is to determine the task-specific measurement uncertainty of CT measurements numerically by using simulations. For this, all relevant influence factors need to be identified and quantified. It is known, for example, that geometrical misalignments of the detector lead to measurement deviations if the reconstruction does not consider these misalignments. This contribution uses computer simulation of CT data to investigate the influence of geometrical misalignments of the detector on several measurands found in typical measurements tasks in the industry. A newly developed test specimen with a broad variety of features is used for this study. Angular and positional detector deviations are systematically introduced into the simulations and deliberately left uncompensated during the CT reconstruction. The resulting measurement deviations are shown and discussed.
The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. Two types of signals were used for guided wave excitation, namely chirp signal and tone-burst signal. The chirp signal had a frequency range of 20-500kHz. The tone-burst signals had a form of sine modulated by Hann window with 5 cycles and carrier frequencies 16.5kHz, 50kHz, 100kHz, 200kHz, 300kHz. The piezoceramic actuator used for this purpose was attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.
The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields.
A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate.
Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.
The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging.
The detailed description of the dataset is published in Data in Brief Journal.
Traceable determination of non-static XCT machine geometry: New developments and case studies
(2022)
It is fundamental to determine the machine geometry accurately for dimensional X-ray computed tomography (XCT) measurements. When performing high-accuracy scans, compensation of a non-static geometry, e.g. due to rotary axis errors or drift, might become necessary. Here we provide an overview of methods to determine and account for such deviations on a per projection basis. They include characterisation of stage error motions, in situ geometry measurements, numerical simulations, and reconstruction-based optimization relying on image quality metrics and will be discussed in terms of their metrological performance. Since a radiographic calibration is always required to provide an initial absolute geometry, this method will be presented as well. The improvements of the XCT geometry correction methods are presented by means of case studies. The methods can be applied individually or in combination and are intended to provide a toolbox for XCT geometry compensation.
A software toolbox is introduced that addresses several needs common to computed tomography (CT). Built for the WIPANO CTSimU project to serve as the reference implementation for its image processing and evaluation tasks, it provides a Python 3 interface that is adaptable to many conceivable applications. Foremost, the toolbox features a pipeline architecture for sequential 2D image processing tasks, such as flat field corrections and image binning, and enables the user to create their own processing modules. Beyond that, it provides means to measure line profiles and image quality assessment algorithms to calculate modulation transfer functions (MTF) or to determine the interpolated basic spatial resolution (iSRb) using a duplex wire image. It can also be used to calculate projection matrices for the reconstruction of scans with arbitrary industrial CT geometries and trajectories. The CTSimU project defined a framework of projection- and volume-based test scenarios for the qualification of radiographic simulation software towards its use in dimensional metrology. The toolbox implements the necessary evaluation routines and generates reports for all projection-based tests.
Development of an Accurate and Robust Air-Coupled Ultrasonic Time-of-Flight Measurement Technique
(2022)
Ultrasonic time-of-flight (ToF) measurements enable the non-destructive characterization of material parameters as well as the reconstruction of scatterers inside a specimen. The time-consuming and potentially damaging procedure of applying a liquid couplant between specimen and transducer can be avoided by using air-coupled ultrasound. However, to obtain accurate ToF results, the waveform and travel time of the acoustic signal through the air, which are influenced by the ambient conditions, need to be considered. The placement of microphones as signal receivers is restricted to locations where they do not affect the sound field. This study presents a novel method for in-air ranging and ToF determination that is non-invasive and robust to changing ambient conditions or waveform variations. The in-air travel time was determined by utilizing the azimuthal directivity of a laser Doppler vibrometer operated in refracto-vibrometry (RV) mode. The time of entry of the acoustic signal was determined using the autocorrelation of the RV signal. The same signal was further used as a reference for determining the ToF through the specimen in transmission mode via cross-correlation. The derived signal processing procedure was verified in experiments on a polyamide specimen. Here, a ranging accuracy of <0.1 mm and a transmission ToF accuracy of 0.3μs were achieved. Thus, the proposed method enables fast and accurate non-invasive ToF measurements that do not require knowledge about transducer characteristics or ambient conditions.
Damage detection of reinforced concrete (RC) structures is becoming a more attractive domain due to the safety issues arising in the last few decades. The damage in concrete can be caused by excessive exploitation of the structure or environmental effects. The cracks in concrete can be detected by different nondestructive testing methods. However, the available methods used for this purpose have numerous limitations. The technologies available in the market nowadays have difficulties detecting slowly progressive, locally limited damage. In addition, some of These methods cannot be applied, especially in hard-to-reach areas in the superstructures. In order to avoid these deficiencies, an embedded ultrasonic methodology can be used to detect cracks in RC structures. In this study, the methodology of crack detection supported with the advanced Signal processing algorithm was proposed and verified on RC structures of various types, and cracks occurring between embedded sensors can be detected. Moreover, different pairs of ultrasonic sensors located in the considered structures are used for the analysis of the sensitivity of distance between them. It is shown that the ultrasonic sensors placed in the range of 1.5–2 m can detect cracks, even when the other methods failed to detect changes in the structure. The obtained results confirmed that diffuse ultrasonic sensor methodology is able to monitor real structures more effectively than traditional techniques.
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
Alternative to conventional transmission-based radiography and computed tomography, X-ray refraction techniques are being increasingly used to detect damage in light materials. In fact, their range of application has been recently extended even to metals. The big advantage of X-ray refraction techniques is that they are able to detect nanometric defects, whose size would lie below the resolution of even state-of-the-art synchrotron-based X-ray computed tomography (SXCT). The superiority of synchrotron X-ray refraction radiography and tomography (SXRR and SXRCT) has been shown in the case of light materials, in particular composites. X-ray refraction techniques also yield a quantitifaction of the amount of damage (the so-called relative internal specific surface) and can well be compared with damage models. At the same time, it is impossible for SXRR and SXRCT to image single defects. We show that the combination of refraction- and transmission-based imaging techniques yields an impressive amount of additional information about the type and amount of defects in microstructured materials such as additively manufactured metals or metal matrix composites. We also show that the use of data fusion techniques allows the classification of defects in statistically significant representative volume elements.
Turbine blades for gas turbines are exposed to extreme working conditions in a demanding environment. In-service inspection, maintenance and refurbishment of the heavily stressed parts is necessary to ensure both safety and efficiency, e.g. based on immersion ultrasound testing (UT).
In the course of NDE 4.0, the European project MRO 2.0 aims to innovate the maintenance, repair and overhaul of turbine blades by linking these with modern digital methods. For this, the goal of this project is to go beyond conventional automated and manual UT testing techniques.
The aim is to measure the actual geometry and wall thickness of the complex shaped parts by applying an adaptive TFM that takes into account the refraction of the ultrasonic waves at the transition from the coupling material (water) to the inspected part (steel). In this setup the phased array probe is held by a robotic arm that allows the part to be scanned while remaining mainly perpendicular to the inspected surface. In this way, even complex geometries can be inspected and a 3D model of the actual condition of the part can be created.
The laboratory setup is equipped with a Vantage 64 phased array instrument from Verasonics Inc. and an industrial robot from ABB. A 64 element linear array probe operating at 10 MHz is attached to the robot.
The focus is on optimizing resolution, reliability and inspection speed, as the reconstructed model will be fed to the digital twin at a later stage of the project and used for targeted repairs. In addition to enhancing the reconstruction algorithms, required probe geometry and the parameters needed to inspect turbine blades with partially thin walls and anisotropic materials will also be investigated.
This talk will describe the 3-year project and present the results of the first year. The main focus will be on the development of the reconstruction algorithms used and the experimental setup.
Alternative to conventional transmission-based radiography and computed tomography, X-ray refraction techniques are being increasingly used to detect damage in light materials. In fact, their range of application has been recently extended even to metals. The big advantage of X-ray refraction techniques is that they are able to detect nanometric defects, whose size would lie below the resolution of even state-of-the-art synchrotron-based X-ray computed tomography (SXCT). The superiority of synchrotron X-ray refraction radiography and tomography (SXRR and SXRCT) has been shown in the case of light materials, in particular composites. X-ray refraction techniques also yield a quantitifaction of the amount of damage (the so-called relative internal specific surface) and can well be compared with damage models. At the same time, it is impossible for SXRR and SXRCT to image single defects. We show that the combination of refraction- and transmission-based imaging techniques yields an impressive amount of additional information about the type and amount of defects in microstructured materials such as additively manufactured metals or metal matrix composites. We also show that the use of data fusion techniques allows the classification of defects in statistically significant representative volume elements.
While the problem of the identification of mechanisms of hydrogen assisted damage has and is being thoroughly studied, the quantitative analysis of such damage still lacks suitable tools. In fact, while, for instance, electron microscopy yields excellent characterization, the quantitative analysis of damage requires at the same time large field-of-views and high spatial resolution. Synchrotron X-ray refraction techniques do possess both features. In this work, we show how synchrotron X-ray refraction computed tomography (SXRCT) can quantify damage induced by hydrogen embrittlement in a lean duplex steel, yielding results that overperform even those achievable by synchrotron X-ray absorption computed tomography. As already reported in literature, but this time using a non-destructive technique, we show that the hydrogen charge does not penetrate to the center of tensile specimens. By the comparison between virgin and hydrogen-charged specimens, we deduce that cracks in the specimen bulk are due to the rolling process rather than hydrogen-assisted. We show that (micro)cracks propagate from the surface of tensile specimens to the interior with increasing applied strain, and we deduce that a significant crack propagation can only be observed short before rupture.
Um aus messtechnisch ermittelten Dispersionsabbildungen geführter Ultraschallwellen Rückschlüsse auf die Materialparameter zu ziehen, werden in der aktuellen Forschung verschiedene inverse Methoden diskutiert. Maschinelles Lernen und insbesondere Convolutional-Neural-Networks (CNNs) stellen eine Möglichkeit der automatisierten inversen Modellierung und Evaluierung von Bilddaten dar. In diesem Vortrag wird anhand synthetischer Daten gezeigt, wie das Ausbreitungsverhalten von geführten Ultraschallwellen unter Verwendung von CNNs genutzt werden kann, um die isotropen elastischen Konstanten einer plattenförmigen Struktur zu bestimmen.
The integral collection of information such as strains, cracks, or temperatures by ultrasound offers the best prerequisites to monitor structures during their lifetime. In this paper, a novel approach is proposed which uses the collected information in the coda of ultrasonic signals to infer the condition of a structure. This approach is derived from component tests on a reinforced concrete beam subjected to four-point bending in the lab at Ruhr University Bochum. In addition to ultrasonic measurements, strain of the reinforcement is measured with fiber optic sensors. Approached by the methods of moment-curvature relations, the steel strains serve as a reference for velocity changes of the coda waves. In particular, a correlation between the relative velocity change and the average steel strain in the reinforcement is derived that covers 90% of the total bearing capacity. The purely empirical model yields a linear function with a high level of accuracy (R 2 =0.99, R2=0.99, RMSE≈90μ
RMSE≈90μ strain).
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
The benefits of including fibers in ultra-high performance concrete (UHPC) are attributed to their good bond with the matrix and, hence, an optimal utilization of their properties. At the same time, though, fiber reinforcement may contribute to anisotropy in the composite material and induce weak areas. The influence of the fibers’ orientation on the material properties is a matter of current scientific discourse and it is known to play a vital role in structural design. In the case studies presented herein, mechanical laboratory tests using pulsating load regimes on UHPC with a strength of more than 200 MPa were simulated by use of finite element models. The orientations of the fibers were measured for each test sample prior to failure using an X-ray computed tomography (CT) scanner, and these orientations are explicitly implemented into the model. The paper discusses the methodology of merging data retrieved by CT image processing and state-of-the-art FE simulation techniques Moreover, the CT scanning was carried out throughout the testing procedure, which further enables the comparison of the mechanical tests and the FE models in terms of damage propagation and failure patterns. The results indicate that the overall fiber configuration and behavior of the samples can be realistically modelled and validated by the proposed CT-FE coupling, which can enhance the structural analysis and design process of elements produced with steel fiber reinforced and UHPC materials.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.
Ultrasonic testing is a widely applied measurement method in materials research and medicine. Commonly, a transducer is coupled to the specimen directly or via a liquid coupling agent. While reducing acoustic transmission losses significantly, this procedure is time-consuming and cannot be used for sensitive specimens. Air-coupled ultrasound is a viable alternative in such cases, although suffering from very high acoustic transmission losses between transducer, air and specimen. The recently introduced fluidic transducer (FT) generates ultrasound by utilizing the instability of a supersonic air jet switched inside a fluidic amplifier. Since only air is used as the working medium and no vibrating surfaces are used for ultrasound generation, the transducer is able to efficiently generate large acoustic pressure amplitudes. The resulting acoustic field shares its directivity with the ejected high-velocity air jet. Thus, the acoustic energy needs to be redirected from the jet axis in order to make the fluidic transducer applicable to sensitive specimens. In this study, the effectivity of using sonic crystals (SCs) for this redirection is investigated using acoustic and flow measurements. SCs are air-permeable while being reflective to large acoustic frequency bands. It was shown that both a defect waveguide and a mirroring strategy successfully redirected the acoustic field from the air jet. Furthermore, the interaction of flow and SC showed strong acoustic quenching if the SC was placed too close to the FT outlet. Blockage of the jet entrainment due to the SC may result in slightly higher off-axis flow velocities locally, which should be considered in sensitive applications.
There are many different methods to characterize air-coupled ultrasonic transducers for non-destructive testing. Data sheets of various manufacturers contain information about some parameters important for the performance of transducers, but this information is not standardized, so that a comparison between probes of different manufacturers is difficult. Therefore, the German Society for Non-Destructive Testing (DGZfP) is working on a guideline to characterization of air-coupled probes.
One of the topics in this guideline is the application of thermoacoustic transducers for the characterization of receivers and another topic is the application of microphones for the characterization of transmitters. In this presentation we compare various characterization methods with the particular focus on the characterization of thermoacoustic transducers using an optical microphone. Both thermoacoustic transmitters and optical microphones have a very large bandwidth compared to conventional air-coupled transducers, but their spectrum is not entirely linear, which needs to be taken into account if they are applied as reference transducers.
Ultrasonic monitoring, making use of the sensitivity of the coda of repeated transmission meas-urements to changes in stress, temperature, moisture, as well as localized or distributed damage, has gotten at-tention in structural health monitoring (SHM) research recently. Analysis methods such as coda wave inter-ferometry (CWI), including its nonlinear extension, have been shown to be able to measure ultrasonic wave velocity changes with a 1∙10-5 resolution, while indicators such as cross-correlation or cross-coherence have been used to distinguish between reversible and irreversible changes. Several small- and large-scale laboratory experiments have demonstrated that stress changes in structures can be captured or damage detected in a very early stage. The use of this technique for pre-warning before failure are currently under investigation, as well as detailed research on the physical causes and the connection between ultrasonic wave properties and materi-al/structural behavior. Recently, several of large-scale laboratory and real structures have been instrumented with embedded ultrasonic transducers to gather experience and evidence on how to use this technology in re-al-world applications. Preliminary results from installations on a new bridge, an existing bridge, a tunnel, a la-boratory earthquake test as well as a historic stadium in Germany, Poland, and the United States, respectively, are presented. Environmental influences (mainly temperature) and validation by load tests are discussed.
Ultrasonic monitoring, making use of the sensitivity of the coda of repeated transmission meas-urements to changes in stress, temperature, moisture, as well as localized or distributed damage, has gotten at-tention in structural health monitoring (SHM) research recently. Analysis methods such as coda wave inter-ferometry (CWI), including its nonlinear extension, have been shown to be able to measure ultrasonic wave velocity changes with a 1∙10-5 resolution, while indicators such as cross-correlation or cross-coherence have been used to distinguish between reversible and irreversible changes. Several small- and large-scale laboratory experiments have demonstrated that stress changes in structures can be captured or damage detected in a very early stage. The use of this technique for pre-warning before failure are currently under investigation, as well as detailed research on the physical causes and the connection between ultrasonic wave properties and materi-al/structural behavior. Recently, several of large-scale laboratory and real structures have been instrumented with embedded ultrasonic transducers to gather experience and evidence on how to use this technology in re-al-world applications. Preliminary results from installations on a new bridge, an existing bridge, a tunnel, a la-boratory earthquake test as well as a historic stadium in Germany, Poland, and the United States, respectively, are presented. Environmental influences (mainly temperature) and validation by load tests are discussed.
NDE 4.0 in Civil Engineering
(2021)
Civil engineering industry is one of the most important industry sectors in the worldwide economy. It contributes significantly to the gross economic product and general employment. Even more important, it provides many of the basic needs of the society (e.g., housing, infrastructure, and protection from natural hazards). The concept of “Industry 4.0” or “Smart Production” has not yet made significant progress in the civil engineering industry. The designing, building, and operating processes are still widely dominated by the exchange of printed documents and drawings. Most objects (buildings and other constructions) are unique, and a large part of the production still requires a large amount of manual labor. As-built documentation and quality assurance are often neglected. Civil engineering is among the industry sectors with the lowest level of digitalization and the lowest gain in productivity. However, this is going to change. In the past decade, several drivers have challenged the ways clients, contractors, and authorities currently operate. These drivers include but are not limited to an increasing demand for serialization and automatization or the mandatory introduction of “Building Information Modeling” (BIM) in public procurement as well as the upcoming use of digital twins. NDE plays an increasing role in quality assurance, condition assessment, and monitoring of structures. However, with very few exceptions, applications are mostly nonstandardized and performed only at selected sites. To change this, the NDT-CE community including manufacturers, service providers, clients, and the scientific community must work consistently on open data formats, interfaces to BIM, standardization, and validated ways for a quantitative use of the results in the assessment of constructions.
Non-destructive testing methods are available in civil engineering for decades to estimate concrete properties or to detect flaws and features. But recently we have seen the dawn of next-generation tools, methods, and applications. Some of them will be discussed in the web talk: – Better tools: deeper and more detailed insight into concrete constructions – Better methods: Quantitative use in probabilistic structural assessment – Better rules: Towards standardization, qualification, and certification – Better application: Digitalization and Elimination of the boundaries between NDT, SHM, and BIM: NDT-CE 4.0 Not enough? I might show, how cosmic rays might become a game-changer in NDT-CE. This live webinar record was provided by https://eurostruct.org
The determination of the measurement uncertainty of quantitative and qualitative results is an important quality management tool, for example to describe measuring equipment, procedures, measurement results and the quality of products. The lecture presents the process description for the determination of the measurement uncertainty according to GUM, the determination of uncertainties in qualitative measurement results and the application of the measurement uncertainty for conformity assessment.
ML has been successfully applied to solve many NDT-CE tasks. This is usually demonstrated with performance metrics that evaluate the model as a whole based on a given set of data. However, since in most cases the creation of reference data is extremely expensive, the data used is generally much sparser than in other areas, such as e-commerce. As a result, performance indicators often do not reflect the practical applicability of the ML model. Estimates that quantify transferability from one case to another are necessary to meet this challenge and pave the way for real world applications.
In this contribution we invetigate the uncertainty of ML in new NDT-CE scenarios. For this purpose, we have extended an existing training data set for the classification of corrosion damage by a new case study. Our data set includes half-cell potential mapping and ground-penetrating radar measurements. The measurements were performed on large-area concrete samples with built-in chloride-induced corrosion of reinforcement. The experiment simulated the entire life cycle of chloride induced exposed concrete components in the laboratory. The unique ability to monitor deterioration and initiate targeted corrosion initiation allowed the data to be labelled - which is crucial to ML. To investigate transferability, we extend our data by including new design features of the test specimen and environmental conditions. This allows to express the change of these features in new scenarios as uncertainties using statistical methods. We compare different sampling and statistical distribution-based approaches and show how these methods can be used to close knowledge gaps of ML models in NDT.