Showing papers in "e-Journal of Nondestructive Testing in 2022"

Improving throughput and image quality of high-resolution 3D X-ray microscopes using deep learning reconstruction techniques

Abstract: In high-resolution X-ray computed tomography (CT), also known as 3D X-ray microscopy (XRM), low photon counts can lead to extremely long data acquisition times (in the order of hours). Reducing the number of radiographic projections (Np) acquired for CT reconstruction can be a cost-efficient solution in some cases. But the risk associated with reducing Np, if analytical filteredbackprojection algorithms are used for CT reconstruction, e.g., Feldkamp-Davis-Kress (FDK), is that it may produce a significant loss of image quality. Typical Np thresholds for a faithful 3D image reconstruction, required by the Nyquist-Shannon sampling theorem, are in the order of thousand projection views with modern XRM instruments. It is now well known, however, that deep learning (DL) based algorithms for CT reconstruction can improve the scan time (throughput) and image quality capabilities of XRM. This paper proposes the use of DL-based algorithms as an option for reducing Np, even down to a few hundred projections, without a significant loss of image quality. The integration of DL-based reconstruction techniques into 3D XRM workflows is presented throughout this article. It is shown that 3D XRM data reconstructions produced by DL-based workflows can provide up to 8X and 10X throughput improvement at similar or better image quality compared to standard FDK reconstruction.

Construction and validation of a novel phased array borehole probe for ultrasonic investigations at sealing structures in radioactive repositories

Abstract: A new type of ultrasonic borehole probe is developed for the quality assurance of sealing structures in radioactive waste repositories using existing research boreholes. The goal is to examine the sealing structures made of salt concrete for possible cracks, delamination, and embedded objects. A prototype probe uses 12 individual dry point contact (DPC) horizontal shear wave transducers separated by equidistant transmitter/receiver arrays, each made up of six individual transducers. It is operated with a commercially available portable ultrasonic flaw detector used in the civil engineering industry. To increase the generated sound pressure of the borehole probe, the number of transducers in the novel probe is increased to 32. In addition, timed excitation of each probe is used to direct a focused sound beam to a specific angle and distance based on calculated time delays. Hence, the sensitive test volume is limited, and the signal-to-noise ratio of the received signals is improved. This paper presents the validation of the newly developed phased array borehole probe by beam computation in CIVA software and experimental investigations on a semi-cylindrical test specimen to investigate the directional characteristics. In combination with geophysical reconstruction techniques, an optimised radiation pattern of the probe is expected to improve the signal quality and thus increase the reliability of the imaging results. This is of great importance for the construction of safe sealing structures needed for the disposal of radioactive or toxic waste.

Comparison campaign of XCT systems using machined standards representative of additively manufactured parts

Abstract: Additive manufacturing enables the production of complex geometries, both internally and externally. This poses a challenge to quality control. Non-destructive volumetric testing methods are required. Among these methods, X-ray computed tomography is currently the most promising. It enables not only to detect inner and outer defects but also to perform dimensional measurements. However, the method lacks of metrological tracability. To fill this gap, an EMPIR project 17IND08 AdvanCT involving several European countries was proposed. In this frame, DTU Mekanik and LNE have conducted a comparison campaign of XCT systems using machined material measures representative of additively manufactured parts. The purpose of the comparison was to investigate the performances of industrial X-ray computed tomography with respect to dimensional measurements on external but also on internal features.

Trajectory Optimization in Computed Tomography Based on Object Geometry

Abstract: We present a new approach for the task of trajectory optimization for X-ray computed tomography (CT) in a robot-based CT (RoboCT) application. An optimized trajectory corresponds to a projection set of predefined size that leads to a 3Dreconstruction with less image-artifacts than other possible sets of the same size. We incorporate prior knowledge through a model of the object and simulated X-ray projections of the interested scenario. We formulate the optimization problem as maximization of an objective function and solve it heuristically by means of a genetic algorithm. As objective function, we use a metric that correlates with the amount of edge information of a projection set, since we assume that this characteristic is important for a reconstruction with low image-artifacts. The metric is based on wavelet analysis of the projections. We carry out computer experiments for a test object made of homogeneous material. The optimized trajectory allows a sharp reconstruction of straight edges and clearly outperforms reconstructions from standard trajectories.

Benchmarking of different reconstruction algorithms for industrial cone-beam CT

Abstract: In the field of industrial computed tomography, there have been a lot of interests in different reconstruction algorithms which could perform better than the standard FDK algorithm in non-ideal conditions leading to faster CT acquisitions. However, the computation efforts required by iterative algorithms are quite high, which makes them less practical in industrial environments. In this work, we propose a benchmark of the performances of various reconstruction algorithms using Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox; the benchmarking criteria used include both image quality metrics and taskoriented post-reconstruction results (measurements, porosity, etc.). Furthermore, we have also tested their performances with limited projections and limited angular range to facilitate faster CT acquisitions. The preliminary results indicate several improvements in the reconstructions (better feature definitions) with one of the iterative algorithms (CGLS: conjugate gradient least squares) as compared to FDK.

Towards automatic crack segmentation in 3d concrete images

Abstract: Concrete is one of the most commonly used construction materials. A deeper insight into its mechanical properties, in particular cracking behaviour, can be gained from stress tests. Computed tomography captures the microstructure of building materials, including crack initiation and propagation in a fully three-dimensional manner. However, the complex microstructure of concrete renders crack segmentation a very challenging task. Both, the validation of segmentation methods and the training of machine learning approaches, are hindered by the lack of reliable ground truth segmentations for real data sets. To overcome this problem, a novel procedure for generating pairs of semi-synthetic images and ground truth was introduced by the authors in a previous study. Using this semi-synthetic data, Hessian-based percolation and 3d U-net were identified as the most promising of eight approaches for crack segmentation. Here, we discuss adaptions of the methods that allow for a handling of additional features observed in real computed tomography data of concrete, in particular local variations in crack thickness.

First insights into the human-related risks of tunnel inspection

Abstract: Whereas human factors in the non-destructive testing (NDT) of metallic components are a poorly investigated topic (in comparison to other industrial fields such as aviation), human factors in the inspection of concrete components are even less known. Studies have shown that there is always some variability between individuals in their inspection results and that human factors affect the reliability of NDT inspections. And even though those human factors (the effects of and interaction between technology, organisation, environment, and individual characteristics) do not necessarily lead to negative inspection outcomes, their understanding is a vital step towards preventing possible structure-breaking failures and thereby ensuring the safety of industry, environment and infrastructure. To identify the possible human-related risks in the tunnel inspection, a systematic approach to risk management has been adopted. This involves identification of risks, their characterisation, and suggestions for risk treatment. In line with this approach, the inspections will be observed followed by interviews with inspectors to collect initial information about the inspections in the field and possible performance shaping factors. Furthermore, a human-oriented Failure Modes and Effects Analysis (FMEA) will be used to identify possible human-related risks throughout data collection and evaluation and to evaluate them with respect to their possible causes, consequences, the probability of their occurrence and with respect to existing and possible preventive measures. The results aim to increase our overall understanding of human factors related to NDT, provide first insights and understanding of human factors in tunnel inspection and suggest measures to prevent human error in that application.

AI-Supported Segmentation of Industrial CT Data

Abstract: The segmentation of datasets has always been a vital part of many image or volume processing applications, especially regarding tomography data. Common approaches nowadays use either manually tuned methods or rely on techniques like Deep Learning. However, such methods often are useful for segmenting only components of a certainly well-defined type which mostly suffices for clinical data but not for industrial applications anymore. In order to overcome these limitations we propose an interactive approach to volumetric segmentation encompassing robust classifiers and localized volume processing. The resulting algorithm is flexible enough to be used for a broad variety of different segmentation tasks while still generating high quality results. The local processing further enables the segmentation of larger volumes which cannot be handled by existing applications.

4D-XCT and Digital Volume Correlation of Flax Fibre Composites Under Compressive Load After Relaxation

Abstract: In-situ testing capabilities of lab-scale industrial computed tomography are rapidly improving. In the present study, the compressive deformation behaviour of a flax fibre-reinforced epoxy composite is studied in an in-situ compression test with stepwise loading, during 3D imaging with X-ray Computed Tomography (XCT). The 3D volumes are processed with Digital Volume Correlation (DVC) to identify the deformation of each loading step. The natural variability of the flax texture served as an adequate speckle pattern for DVC. The precision and accuracy of the DVC were investigated prior to the analysis of the deformation volumes by digitally deforming the reference undeformed volume with a known strain of 2 %. The calculated global strain was in good agreement with the digitally applied strain. Subsequently, DVC was applied to the acquired reference and deformed volumes and the analysis was focused on the 3D strain field evolution between the consecutive loading steps. Strain localization regions were observed in the axial strain εzz and the in-plane strain εxx. In the subsequent loading steps, the onset of compressive fibre-kinking is observed as a rotating fibre inside the magnified strain region. The 3D strain field evolution allows for a better understanding of the deformation and failure mechanisms at the meso/micro-scale in natural fibre reinforced composites under compressive loads.

Influence of detector misalignments on different geometrical and dimensional measurands using a dedicated test specimen

Abstract: 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.

Monitoring – pilot study duraBASt-bridge

Abstract: The federal highways are facing major challenges due to the increased traffic volume and the age of the structures. A replacement of all structures is not possible in the short term, the implementation of monitoring offers the possibility of continued use of the bridges. Currently, monitoring on road bridges is rarely used. Within the framework of the pilot study "duraBASt bridge", various possible applications of monitoring und non-destructive testing methods are being tested on a prestressed concrete bridge at the Cologne East highway. The determination of the durability by means of moisture and corrosion measurements with different sensors is being tested. Furthermore, the possibilities of integrating monitoring into BIM are being investigated. In the future, the creation of a Digital Twin is planned. In a first step, a finite element model is to be created, which is to be calibrated by means of load tests using the strain measurement sensors already installed in the roadway. For the load tests the fibre-optic sensors and strain measurement sensors installed in the bridge are to be used. Non-destructive testing methods were used to locate the tendons of the bridge and determine the thickness of the pavement structure.

Assessing the influence of CT acquisition parameters on flaw detectability through simulation

Abstract: X-Ray Computed Tomography (XCT) is a unique tool to fully visualize and understand the nature and size of flaws in industrial parts, with a growing application in different fields such as aeronautics and more recently metal additive manufacturing. The inevitable questions underlying any XCT inspection concern the detectability limit of the measure. What size of defect will be detected with my current configuration? How can I optimize my acquisition material or parameters to improve the detectability limit? Naturally, the known characteristics of the XCT systems (detector pixel size, X-ray tube voltage and focal spot size, magnification) give a first answer to these questions, at least in terms of spatial resolution, but it is more difficult to estimate a priori the visibility of a flaw in terms of contrast. The cost of XCT inspection, the difficulty to design specimen with narrow internal defects and the influence of the geometry of the part on the XCT image quality make experimental analysis of the detectability limits difficult. The simulation brings therefore a promising alternative, provided that it gives a thorough representation of a real inspection.

Engineering of Existing Structures: The Need and Place for Non-destructive Evaluation (NDE)

Abstract: Structural engineers today are still educated mainly to design new structures. This ignores the fact that structures already exist and need to be maintained. Students are thus often ill-equipped to deal with maintenance and preservation of existing structures. As an example, when a structural analysis model of an existing structure such as a bridge is built to predict its behavior, it is often done without considering the actual behavior or properties - this would require the engineer to have skills and knowledge in the evaluation of existing structures. As a result, these models are often inaccurate, which can lead to unnecessary replacements of structures. This is not only costly but it also significantly impacts the environment. In this paper we argue that new educational programs that specifically address this need should be developed that revolve around the idea of “engineering of existing structures”. Such programs would still include certain traditional structural engineering courses but also discuss structural behavior and materials properties, and how these can be determined by means of non-destructive testing (NDT) and structural health monitoring (SHM), in sum referred to as non-destructive evaluation (NDE). Because these tools are not traditionally included in the structural engineering curriculum, a new set of basic interdisciplinary skills from the domains of mechanical and electrical engineering, as well as computer science (data analysis, signal processing, etc.), need to be acquired by the students. This paper makes a case for the need of this new direction, proposes curriculum adjustments, and gives practical course examples.

Performance of acoustic emission and digital image correlation for fracture analysis in cementitious mortars under fatigue loading

Abstract: Fatigue loading in brittle materials introduces damage at a micro-scale. These micro-fractures can accumulate and cause a significant reduction in material stiffness or even lead to structural failure. Deformation-based monitoring techniques can be inadequate when detecting damage at a micro-level. Hence, here is where advanced nondestructive testing (NDT) methods such as acoustic emission (AE) and digital image correlation (DIC) can play a great role. The paper aims to combine AE and DIC for advanced fatigue damage analysis on a cylindrical sample subjected to a fatigue Brazilian splitting test. The damage progress quantified from cumulative AE event count, and horizontal displacement measured with DIC showed a very good correlation. The damaged region was identified with an AE localization plot and with a DIC displacement field plot, the damaged area was well represented by both techniques. When a material goes through an irreversible change, part of the released energy will generate an elastic wave. The wave will propagate through the material and the whole phenomenon is termed an acoustic emission. By using AE sensors on the surface of the sample, these waves are captured and analyzed to better understand the source of the emission. Its high sensitivity to micro-changes makes it an ideal technique to use for fatigue damage investigation. Digital image correlation relates sequentially taken images to determine displacement and strain measurements. While DIC analyzes material evolution using surface deformation measurements, AE on the other hand is capable of monitoring damage inside the volume of material. Hence, their combination provides an efficient test setup for monitoring fatigue micro-fracture. The paper aims to combine AE and DIC for advanced fatigue damage analysis on cylindrical samples that are subjected to monotonic and fatigue Brazilian splitting tests. The analysis will use damage progress under monotonic loading as a reference to further understand fatigue fracture. Methods of AE data filtering and AE source localization are discussed and evaluated for their use in fracture analysis. The utilization of DIC to track the material stiffness evolution is investigated. In addition, the paper compares fracture process zones quantified by AE and DIC. From the experimental analysis, it was found that both methods captured the fatigue damage patterns, yet provided complementary information for damage evaluation.

Deep Learning for improving the efficiency of dimensional measurement workflows with high-resolution X-ray computed tomography

Abstract: High-resolution X-ray computed tomography (CT) instruments, also known as three-dimensional (3D) X-ray microscopes, can be adapted for dimensional metrology applications such as geometric dimensioning and tolerancing of metallic components. However, CT scanning times can be prohibitively high for industrial measurement inspection tasks owing to the poor contrast from X-ray attenuation in Ferrous metals, especially if the measurement of spatial resolutions under 5 µm are required. This paper describes a software-defined approach to dramatically reducing total exposure time (or scanning time) while maintaining resolution loss within 2 micrometers as compared to the baseline scans acquired over 6 hours. Here, we combine two deep learning (DL) codes in our surface extraction workflow to compensate for lower signal-to-noise ratio in short exposure data (acquired with lower number of projections): (1) a surface determination (post-reconstruction) , and (2) a denoising algorithm (pre-reconstruction). Training data was acquired from a scan of an 8-hole automotive fuel injector (sample 1) with a 165 µm nominal diameter per hole. For testing the accuracy of the workflow, a separate scan of a 6-hole side-mount injector (sample 2) was acquired. For both samples, the acquired X-ray projections (or radiographs) were binned down to 10X such as to simulate faster scans. For training and testing workflows, the full exposure scans (baseline) were used as target and the shorter exposure scans as inputs to the deep learning models. To determine loss of surface accuracy from the baseline case, a metric is formulated (in micrometers) and the trends are reported for when the total measurement time was reduced by up to 10X (up to 0.6 hours, using only 360 projections). We report that scan times can be reduced by over 10X while retaining the limiting the resolution loss to under 1 micrometer.

Influence of X-Ray Radiation on Historical Paper

Abstract: Historical books are often so fragile that they cannot easily be opened in order to read the contents without damaging them. Current research shows, that industrial X-ray computed tomography (CT) can be used to examine closed historical manuscripts. However, the effect of X-rays on ancient paper has rarely been studied, making it difficult to assess the impact of CT measurements on fragile historical manuscripts in terms of additional destruction. To address this problem, various types of paper were exposed to high levels of X-ray radiation in order to examine them for change in optical properties. The investigations showed increasing yellowing due to the increased radiation energy. However, the severity of the yellowing is strongly dependent on the composition of the paper.

Cross-Virtuality Analysis of Rich X-Ray Computed Tomography Data for Materials Science Applications

Abstract: This work explores combining classical desktop-based analysis systems with virtual reality. In this context, we provide a system for exploring objects, in particular fibers and pores, along with their characteristics, such as length, orientation, or shape. This system employs visualization methods in virtual reality focusing on the detailed spatial impression, combined with desktop-based methods focusing on analyzing characteristics and their distributions. We provide two case studies based on fiber-reinforced polymer (FRP) datasets, showcasing the potential of our system. One case study analyzes FRP containing short, strongly curved PET fibers, a second one utilizes the prototype to explore elliptic pores inside an FRP dataset. Our preliminary results show that the addition of virtual reality to the classical desktop-based workflow can enhance the spatial understanding of the dataset, as well as foster engagement with the data.

Full-waveform inversion of ultrasonic echo signals to evaluate grouting quality of tendon ducts in post-tensioned concrete structures

Abstract: Reliable techniques for non-destructive evaluation of reinforced and prestressed concrete structures are of utmost importance for the maintenance of aging infrastructure. A particular area of interest is the evaluation of grouting quality within tendon ducts in post-tensioned concrete. Detecting voids and water-filled cavities in plastic or metal ducts is challenging, especially at greater depths or in the vicinity of rebar. Conventional non-destructive inspection methods based on elastic waves, such as impact-echo or ultrasonic pulse echo method using synthetic aperture focusing and/or signal phase analysis, often lack sensitivity to those defects and/or rely on manual and subjective interpretation of the complex data. To overcome these problems, we present a synthetic feasibility study based on full-waveform inversion for reliable ultrasonic non-destructive testing. Full-waveform inversion is a powerful imaging technique that infers tomographic reconstructions of the material properties from ultrasound measurements. The method is widely used for geophysical applications based on seismic waves and has recently gained increasing attention for ultrasonic inspection applications. Using digital twins, we demonstrate the effect of voids and water inclusions on simulated ultrasonic echo signals. This information can be used in an iterative inversion algorithm to create a 3-D quantitative model of the specimen’s interior. We showcase in several numerical examples that even under uncertainties regarding aggregate size distribution and location of the rebar, full-waveform inversion can reveal defects related to the tendon ducts, which enables a robust and automated assessment of the grouting quality.

A BIM–based Augmented Reality System for Four-Dimensional Simulation

Abstract: Current 4D simulation only uses the main structure of a construction project as demonstration content, meaning that integration with the surrounding environment is lacking. Moreover, the solution reached may not necessarily be the optimal one. Therefore, if the factor of the surrounding environment can be considered in the discussion process, the efficiency of 4D simulation can be enhanced. To address this problem, this study integrated BIM and augmented reality (AR) technologies. The surrounding environment was incorporated into the structure model, and 4D simulation was performed on the construction environment. By using BIM and the Unity game development engine, augmented reality (AR) systems were constructed to enable the integration of real-world environments with smart devices.

Real-time bridge monitoring using ultrasonic techniques combined with six-component (6-C) measurements

Abstract: This study aims to develop a real time structural health monitoring method by ultrasonic tests combined with advanced six component (6C) translation and rotation measurements. Conventionally, the investigation of the velocity and acceleration response in the translation direction is used to obtain the eigenfrequencies of structures. Recently the measurement of rotation has been considered to fully characterize the dynamic behavior of structures. This research undertakes the evaluation of a novel 6C sensor (IMU50-iXblue) with components originally developed for navigation for the purpose of bridge monitoring. However, as for all vibration recordings, there is a certain influence of environmental conditions (mainly temperature) which may affect evaluation and the results of structural assessment. We propose applying the cross-correlation function to the 6C ambient vibration signals to reconstruct wave propagation and using coda wave interferometry (CWI) to obtain internal velocity variation from waveforms. A field experiment on a large-scale prestressed concrete bridge model is presented. To verify that we are able to identify the pre-stress loss even in presence of temperature effects, we perform measurements in two different scales: the ultrasonic and output-only, vibration measurements. The change in the structural properties due to the pre-stress loss should be detected by the pulse velocity change. The results reveal both the performance and advantages of ultrasonic techniques and the capabilities of 6C sensors. To conclude, the application of CWI to wave signals contributes to a comprehensive assessment for bridge monitoring.

Novel dedicated reference standards for the qualification of radiography-based computed tomography simulation software

Abstract: 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.

Internal Surface Roughness Measurement of Metal AM Channels via X-ray Computed Tomography: A Case Study

Abstract: Additive manufacturing (AM) is able to create engineering components with internal features that are unmeasurable using conventional tactile and optical methods; new methods for measuring internal inaccessible features in a non-destructive manner are required. In this work a metal additively manufactured (AM) component with an internal channel is fabricated and scanned via X-ray computed tomography (XCT) before and after undergoing internal surface polishing by means of abrasive flow machining (AFM). The internal surface roughness of the AM channel is characterised in 3D to better understand and control the AFM process. The results show that XCT is able to reveal the complex internal surface texture of metal AM components and to quantify the changes caused by the AFM process.

X-ray speckle-based dark-field imaging of water transport in porous ceramics

Abstract: The evaluation of liquid transport through porous ceramics are of high importance in numerous applications of these materials, ranging from chemical and physical filters to biomaterials. We present a proof-of-concept of the capability of speckle-based Xray dark-field imaging (XDFI) for studying the water transport through porous materials with high sensitivity and sub-pixel resolution in a laboratory. Speckle-based imaging (SBI) takes advantage of a simple and flexible setup, with only an additional and inexpensive textured mask, to provide complementary multi-contrast images. Porous ceramic samples with different pore size ranges were imaged in dry and different pure water-saturated states, via an X-ray speckle-tracking setup. The retrieved darkfield images revealed a high sensitivity to (1) the pore size range and to (2) the local water saturation degree. Independently of the pore size range, the dark-field signal decreased upon water saturation. Compared with previously reported laboratory-scale XDFI results for water transport through porous materials, the speckle-tracking approach allows achieving higher temporal and spatial resolutions, thus broadening the range of (water) transport processes which can be investigated without using any contrast agent.

Ultrasonic quality assurance for underground salt shotcrete sealing structures in Teutschenthal mine, Germany

Abstract: Underground sealing structures are necessary to seal radioactive or toxic waste in underground repositories. Particularly developed MgO concrete is used in the mine in Teutschenthal and applied at a large-scale test with the aim to provide concrete with low permeabilities. The test structures (up to 10 m long) were created with the help of a shotcrete procedure. Besides destructive tests, non-destructive ultrasonic measurements are used for quality assurance to monitor potential anomalies such as cracks, concrete inhomogeneities, and delamination. We show results of ultrasonic measurements with a multi-transducer system used at the front site of the concrete structure. Images are obtained by synthetic aperture focusing techniques. Concreting sections are not systematically imaged so that a successful concreting is assumed as also indicated by observed low permeabilities. Several reflectors up to a depth of 1.2 m are identified and interpreted as potential damages in the concrete. Phase analysis of prominent reflectors reveal a negative impedance contrast thus indicating air filled voids, cracks or delamination. Boreholes through some of the identified reflectors are used to verify the results from ultrasonic measurements. Additionally, an experiment with incorporated defects is undertaken to analyse the reliability of the applied methods. Overall, the conducted tests show the great potential of ultrasonic measurements to detect critical anomalies. Despite challenges at small-scale structures (cm-order), large-scale anomalies can be identified. Implementing ultrasonic monitoring during and after the construction of concrete sealing structures is recommended as a tool for quality assurance.

Detecting Changes in Boundary Conditions based on Sensitivity-based Statistical Tests

Abstract: Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e. changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e. Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliability-based approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data from the undamaged structure—in an unsupervised learning mode—making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation.

Virtual CT test environment used to study the effect of individual influence factors

Abstract: Computed Tomography (CT) features a complex measurement chain and the measurement accuracy relies on selecting suitable scan parameters, which are very dependent on the measuring task. To develop a broader understanding of the effect of a given influence factor on the measurement of dimensional features, through a what-if analysis, the use of computer simulation has to be considered as a reasonable alternative to the analytical and experimental approaches. In this regard, this research focuses on the construction of a digital twin of a CT instrument designed for dimensional measurement using a commercial simulation tool and the utilization of the virtual measurement scene to increase the understanding of the relationships between input parameters and dimensional quantities of a mechanical test object. The main outcomes of the performed simulation-based sensitivity analysis and their correlation with some results obtained with the physical CT instrument are described as well.

Correlative Laser Confocal Microscopy Study and Multimodal 2D/3D Registration as Ground Truth for X-ray Inspection of Internal Defects in LPBF Manufacturing

Abstract: In a time when engineers working in the additive manufacturing field are interested in the standardized x-ray computed tomography (XCT) image analysis workflow, an insight into a higher resolution imaging and ground truth validation become invaluable. In this work, we propose a repeatable and automated 2D/3D registration protocol between an XCT volume and a laser confocal microscopy image, thus allowing a correlative multiscale validation and comparison study of the flaw detection capabilities and uncertainties of an XCT analysis of additivelymanufactured parts. Once the spatial correlation achieved, a comparison study evaluating the level of confidence of the flaw detection and measurement computed from the XCT volume is presented. To this end, a pore-to-pore comparison between the XCT volume and the laser confocal image, which offers a 4 times higher resolution as well as a better signal to noise ratio, is carried out and various pore morphology metric distributions are compared. The generality of the proposed approach is ensured by the use of printed Ti64 LPBF samples with different levels of the intentionally seeded and controlled porosity.

Comparison of post-process X-ray computed tomography and in-process optical measurements for defects evaluation in additively manufactured metal parts

Abstract: Despite the capability of fabricating complex and customized components, metal laser powder bed fusion (LPBF) is still affected by manufacturing issues, which can lead to significant geometrical and dimensional errors and internal defects. These aspects can represent a major barrier to a wider industrial application of LPBF, particularly if considering that relevant applications of additive manufacturing are in sectors such as biomedical and aerospace, which have stringent requirements in terms of defects and product quality. The requests for precision improvement are orienting research activities towards the development of inprocess monitoring systems able to perform accurate analyses during the fabrication itself, hence providing useful information for improving the quality of produced parts. To this aim, several in-process monitoring methods have been proposed in the literature to identify and correct out-of-control process conditions. In spite of the aforementioned research efforts, work is still needed to reliably correlate in-process measurements to actual defects. The focus of this experimental study is the definition of a robust methodology to compare in-process optical acquisitions to post-process X-ray computed tomography (XCT) measurements of actual defects. XCT unique capabilities are therefore exploited to support and improve the LPBF process through the implementation of an accurate comparison methodology.

Quantum Computing and Computed Tomography: A Roadmap towards QuantumCT

Abstract: Quantum computing (QC) is considered as a rising star of computing technologies with very promising possibilities towards novel solutions of even more complex computing tasks than those which are tackled using today’s supercomputers. However, direct use in everyday applications is lacking both: large scale quantum computers and quantum algorithms, i.e. software. We are carrying out the first project on a road towards QC enabled Computed Tomography (CT). In our paper we present this project after its first out of five years duration and share the first steps we made with the community. It is worth to mention that the hardware for QC is still in a phase of development, which implies that most of the software research is in a phase of becoming ready for productional use cases.

Improving the dimensional accuracy of computed tomography data obtained with high-resolution 3D X-ray microscopes

Abstract: Today, 3D X-ray microscopes (XRMs) have the unique ability to achieve higher resolution, non-destructive imaging, within larger parts than traditional X-ray micro computed tomography (CT) systems. Such unique capability is, more and more, of interest to industrial quality control entities as they grapple with small features in precision manufactured parts for various industries such as automotive, electronics, aerospace, medical devices, and additive manufacturing, to name a few examples.