Research Jan De Beenhouwer

Abstract

During the X4Food IOF-POC project, software tools will be developed for the purpose of enhanced X-ray inspection. The initial target application will be the internal quality control of fruit and vegetables. While automated classification based on food surface characteristics is already possible, internal quality is still often checked through destructive inspection (cutting open a selection of products), which is both wasteful and restricted to batch control (contrary to 100% inspection). X-rays, due to their material penetration capacity, can be used to inspect internal quality in a non-destructive manner. To this end, an automated X-ray inspection pipeline will be developed to efficiently go from a scanned image to a decision (such as keep/reject in the case of healthy/unhealthy food). Additionally, a digital twin X-ray scanner will be developed in order to generate synthetic X-ray data that can be used to train AI deep learning classification models. Next to the software development, extensive voice-of-customer research will be conducted to improve upon the already existing market understanding and contact potential customers.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Additive Manufacturing has triggered a revolution in the fabrication and design of objects. However, traditional 3D printing methods remain limited in their ability to create dynamic structural components such as soft grippers, selfassembly systems, or smart actuators. This limitation initiated research and development of 4D printing, a technique which uses smart materials that are processed during 3D printing. Such materials possess properties that can be altered in a controlled and reversible manner in response to external stimuli such as temperature changes, magnetic fields, light, or exposure to chemicals. Despite the staggering potential of 4D printing adequate tools are lacking to dynamically study, inspect, and optimise smart materials. Recognizing the need for a solution, we will focus on nurturing the next generation of innovators in the field of time-resolved, lab X-ray imaging, a rapidly evolving technology ideally suited to study the dynamics of smart materials. Challenges include the development of 1) targeted strategies for 4D X-ray computed tomography (4DCT) imaging, 2) 4DCT quality control methods, and 3) lab-based phase contrast imaging for low-contrast advanced materials characterisation. To address these challenges successfully, a multidisciplinary approach is required, incorporating expertise from diverse fields such as applied mathematics, physics, materials science, and product design.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Our manufacturing industry's competitiveness strongly relies on smart digital factories that reconcile increasing quality requirements with higher manufacturing speeds and decreasing lot-sizes for complex and customized products. These trends impose major challenges on quality inspection systems that shall be at the same time fast, autonomous, and highly accurate to fulfill the requirements for certified quality in multiple sectors (e.g., automotive, medical, aeronautics,…). X-ray Computed Tomography (XCT) has a key role to play, as a sole technology enabling simultaneous material defect analysis and dimensional metrology of complex geometries. Unfortunately, current XCT workflows require 1) ample expert intervention to optimize XCT settings, 2) long scan times for acquiring a large number of radiographs to enable high quality 3D reconstructions, and 3) error-prone mesh extraction from the reconstructed images for comparison to the original CAD model. The trade-off between XCT speed, quality and autonomy remains hitherto unsolved, which severely hinders a widespread application in industrial processes. The CAD-InspeX project addresses this challenge and proposes a paradigm shift in XCT-based inspection by developing 1) a digital twin for CAD-based XCT inspection, optimizing scan quality with minimal expert input, 2) optimal inspection design strategies that allow high scanning speeds by minimizing the required set of radiographs, and 3) methods to directly estimate workpiece dimensions and detect defects (e.g., porosity) from this minimal set of radiographs without conventional image reconstruction. If successful, CAD-InspeX is expected to substantially reduce setup time and yield a 10-fold scan time reduction compared to the current XCT-based inspection workflow, without compromising accuracy.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Atom probe tomography (APT) is a nanoscale destructive material analysis method in which a sample is evaporated in a high electric field (field evaporation). It provides a unique and coherent quantification of atomic species with respect to their locations within the sample and is relevant for the identification of nanometric features. It has a wide range of application areas in materials science. However, to analyse the volumetric distribution of atoms within the evaporated samples, the hit maps obtained with APT need to be spatially reconstructed. The standard reconstruction method does not take into account the physical quantities involved in evaporation, resulting in artefacts near regions of interest that degrade spatial resolution. New, alternative methods fail to capture the nanostructure or cannot reconstruct samples of realistic size within a feasible time frame. Consequently, the potential of APT remains largely unexploited. My goal is to develop the methods needed to create a fast and spatially accurate reconstruction operator using a time-reversed integration scheme based on a physically rigorous forward operator that models field evaporation. The use of advanced volumetric meshing and simulation tools will ensure time efficiency while maintaining sub-nanometre accuracy.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan
• Fellow: Lüken Julian

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

3D printing, and additive manufacturing in particular, has in the last couple of years transformed from a prototyping to a more mature manufacturing technology. The driving force for this transformation was the need for custom built, lightweight parts and a more efficient use of raw materials in order to reduce waste. The current trend is towards increasingly complex parts, consisting of multiple materials to enhance their physical and/or mechanical properties. The complexity of the parts brings with it a need for specialized inspection and quality control methods. In this project, the complementarity between different imaging technologies such as 3D x-ray imaging, thermal imaging and terahertz imaging will be explored within the application field of 3D inspection and metrology of 3D printed parts.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Billen Pieter
• Co-promoter: Sijbers Jan
• Co-promoter: Vanlanduit Steve
• Co-promoter: Van Passel Steven

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

X-ray Computed Tomography (XCT) can visualise internal and external characteristics of an object in 3D in a non-destructive manner. To assess the potential of this technology for the inspection of the occurrence of corrosion on maritime structures, covered with non-metallic layers, such as coatings, macrofouling and calcium deposits, a series of lab experiments will be set up to create a set of reference XCT images linked to well-described corrosion processes. Validation will occur using a time series of metal coupons in S235 and 316L, exposed to marine conditions in the Port of Ostend.

Researcher(s)

• Promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Conventional X-ray imaging based on attenuation contrast is widely known through e.g. medical chest radiographs. By acquiring thousands of X-ray radiographs, a 3D representation of the microstructure of an object can be reconstructed, which has many applications in industrial inspection and metrology of manufactured materials. X-ray imaging offers two more contrast types: phase contrast (due to refraction at interfaces) and dark-field contrast (ultra-small angle scattering). The latter two effects can only be observed with specialized grating hardware, such as in the edge-illumination (EI) technique. Phase contrast can be up to 1000 times stronger than attenuation contrast for 'soft' materials, such as polymers. Unfortunately, EI requires at least three measurements to separate the different contrasts, leading to long processing times. This project aims at exploiting phase contrast properties for efficient inspection and metrology of manufactured objects. To limit the number of measurements, algorithms equipped with prior knowledge in the form of 3D mesh models of investigated samples will be developed. Few-view inspection and metrology techniques will then be developed in which measured phase contrast radiographs are compared to simulated radiographs from the reference CAD projections. To enable metrology, adaptability of the surface mesh to acquired radiographs will be implemented. The methods will be validated on manufactured objects containing plastics and metals.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan
• Fellow: Merckx Jannes

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

This core facility integrates top quality infrastructure and unique expertise in X-ray imaging for the reconstruction, processing and analysis of dynamic 3D scenes. It utilizes complementary platforms for 4D X-ray imaging, including an ultra-flexible and multi-modal X-ray CT system (FleXCT) and a stereoscopic high-speed X-ray videography system (3D2YMOX). The facility offers customized services for image acquisition-reconstruction and analysis for both industrial and (in-vivo) biological studies.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: Aerts Peter
• Co-promoter: De Beenhouwer Jan
• Co-promoter: Dirckx Joris
• Co-promoter: Van Wassenbergh Sam

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

X-ray computed tomography (XCT) is widely used in material sciences and industrial applications (e.g., non-destructive testing and inspection) for non-invasive imaging. While traditional XCT relies on absorption of X-rays to generate image contrast, phase contrast X-ray imaging allows to additionally measure the local scattering power in the sample as well as the phase shifts of the X-rays. Edge Illumination (EI) is a phase contrast imaging technique suitable for use with conventional, polychromatic X-ray sources, which has demonstrated its great potential for translation into real-world environments. Unfortunately, adoption of EI-XCT in industry is slow since it requires (up to 4 times) more X-ray data to be acquired, leading to substantially higher acquisition times compared to traditional XCT. In this project, I will develop acquisition and reconstruction methods for EI-XCT with scan times comparable to those of traditional XCT, while still providing the three complementary contrasts. Furthermore, the acquisition method is paired with a dedicated iterative reconstruction algorithm for increased quantification. Achieving a fast and quantitative EI-XCT will increase the potential for EI-XCT to be industrially deployed.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan
• Fellow: Huyge Ben

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Within the field of security, Customs and Border inspection have not had breakthrough technological developments in the last 20 years, since the introduction of X-ray screening. The limits of these current technologies are accentuated by the increasing diversity and novelty in trafficking materials, tools and methods. These limitations combined with the growing needs of inspection and control call for a disruptive innovative solution. Wanting to move a step up from the existing planar scanning methods with limited material identification results, several studies have identified potential solutions focused on: - High energy 3D X-ray tomography - Neutron interrogation/photofission - Nuclear resonance fluorescence (NRR) While these show good results and performances, they also have several important drawbacks, which limits their possible uses. Moreover, these solutions do not have common technological bricks meaning they can only lead to separate disposals. The proposed MULTISCAN3D investigates a new all-in-one system whose purpose is to become simultaneously a user-friendly, flexible, relocatable solution offering high-quality information for: - Fast high energy 3D X-rays tomography (as first line) - Neutron interrogation/photofission (as second line) - Narrow gamma ray beam based NRR (as second line) MULTISCAN3D will start by investigating and defining needs and requirements, in a technologically-neutral way, with Europe's most prominent Customs Authorities which will be translated to technical specifications. The main body of the research will be focused on three parts, following which, lab validations and real-environment demonstration will be carried out. These three work areas are: - Laser-plasma based accelerators as X-ray sources - 3D reconstruction for multi-view configurations and data processing - Detectors and source monitoring At the same time complementary techniques with chemical and SNM identification capabilities will be investigated.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Q-INSPEX aims at the development of novel imaging and image processing protocols to non-invasively and quantitatively inspect objects and subjects. Core imaging technologies herein are X-ray, (near)-infrared, and TeraHertz imaging. These technologies are largely complementary to each other and can be used in different set-ups as (i) an R&D tool to measure specific characteristics of materials (e.g. food structures or polymers), (ii) as a quality control procedure implemented within an industrial setting (i.e. compatible with processing speeds) or (iii) in-field inspections of crops and infrastructure (e.g. corrosion). Furthermore, they can be applied in a wide variety of domains: additive manufacturing, composites, art objects, textiles, archaeology, crops, food, etc.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan
• Co-promoter: Janssens Koen
• Co-promoter: Scheunders Paul
• Co-promoter: Steenackers Gunther
• Co-promoter: Van der Snickt Geert
• Fellow: De Samber Björn
• Fellow: Levrau Elisabeth

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Additive manufacturing (AM) is driving a design and industrial revolution in sectors such as aerospace, energy, automotive, medical, tooling and consumer goods. By 2027, the AM market is expected to almost triple in size to over €30 billion. In AM, ~30% of parts are made layer-by-layer by fusing metal or polymer with a high-power laser. This laser-based AM (LBAM) includes the most common printing techniques: laser powder bed fusion (LPBF) for metals; selective laser sintering (SLS) of polymers. LBAM has limitations in both productivity and efficiency. Roughly 10% of prints must be scrapped due to various defects, resulting in costly post-build inspections as target applications are typically high-end and safety critical. Therefore, the clear need is to improve print quality and reduce costly scrap. Newer LBAM printers increase productivity by having multiple lasers fuse material. This introduces new defect formation mechanisms: misalignment between scan fields; thermal interactions between nearby lasers; interference from spatter particles; laser diffusion from nearby vapor plumes. All lasers need to be controlled in a coordinated way to avoid these defects by a higher level of control over local thermomechanical conditions. Also, the monitoring system needs to cope with higher data rates. Traditional off-axis monitoring systems require one camera per laser, and this solution does not scale well (prohibitive cost, installation complexity). An off-axis in-line monitoring system obviates the need for a dedicated sensor per laser, resulting in a scalable solution and making the monitoring system integratable to existing machines. No such system exists. The above needs and gaps in state-of-the-art (SOTA) translate to the following research themes tackled by MultipLICITY: 1) Fusing information from multiple sensor types to detect a wider range of defect producing conditions. This data fusion needs to operate in a real time control loop, thus requiring research on resource-constrained fusion and analysis algorithms; 2) Generic, multi-material type printer control and defect detection, requiring limited or no retraining. MultipLICITY aims to increase the quality, productivity, and efficiency of LBAM by expanding in-line monitoring and control to multiple defects, multiple materials, and multi-laser systems, at a competitive cost.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

With this project we aim to acquire fast terahertz detection hardware as part of a full-field imaging system. With the new imaging equipment, we will be able to acquire new experimental data that is vital for research in CT reconstruction algorithms and we will be able to extend tomographic reconstruction concepts from x-ray imaging towards the THz domain.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Electrical impedance imaging using high-density micro electrode arrays (HD-MEAs) is an emerging non-invasive technology to monitor cell cultures. The intention of this project is to develop a practical electrical impedance tomography (EIT) strategy for 3D imaging of cells cultured on 2D HD-MEAs.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Atom probe tomography is an analysis tool in materials science that allows to inspect the 3D chemical composition of needle shaped samples at the nano scale. The method works by field-induced evaporation. Ions are then consecutively emitted from the apex of the needle and are absorbed by a position sensitive detector. The result is a tomographic, atomically resolved image of the evaporated volume, represented as a point cloud in which each point is an atom. The current reconstruction approaches however were developed with homogeneous samples in mind and do not account for the complex shape of the sample surface, which evolves during the field evaporation process. The goal of this project is to develop new reconstruction methods that take the shape into account.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In the last decades, metal additive manufacturing (AM) gradually evolved from a rapid prototyping technology to a promising transformative manufacturing technology. In the SBO-FWO DREAM project the UAntwerp research groups Visionlab and InViLab will tackle two important challenges that can transform AM into a reliable manufacturing solution for structurally loaded components: 1) the development of innovative online process monitoring solutions for increased part quality and 2) the development of advanced time-resolved computer tomography measurement techniques for part validation. The activities of the AM2 SEP research project will be integrated into the SBO-FWO DREAM project (if the re-submission of October 2021 is granted). A post-doctoral researcher will be hired to explore links between the technologies used in the two challenges of the project (process monitoring and part validation). In the case that the FWO SBO DREAM project is not granted, we will link with the AM group of VUB through the ongoing FWO SBO project HiPas, which has a similar scope, but with less focus on online process monitoring compared to the DREAM project. In addition, we will use the AM2 SEP budget to lift the SBO-FWO DREAM project to a European level (we have already identified two relevant calls: "HORIZON-CL4-2022-DIGITAL-EMERGING-01-05: AI, Data and Robotics for Industry optimisation", "HORIZON-CL4-2022-DIGITAL-EMERGING-01-03: Advanced multi-sensing systems"). To do so, we might hire a consultant to support us in the submission of a Horizon Europe RIA project. The work of the consultant will include performing a partner search, communication with partners and administrative support for the proposal preparation.

Researcher(s)

• Promoter: Vanlanduit Steve
• Co-promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

Terahertz (THz) tomography is an up and coming technology that uses electromagnetic radiation with terahertz frequency for tomographic imaging. Like X-rays, THz waves provide information about the interior of an object through interaction with the object. THz waves interact with many materials in different ways. They are absorbed in polar materials such as water, penetrate most packing materials (plastic, paper, ceramics, …) and are completely reflected by metal. In contrast to X-rays, there are no known negative effects of THz waves, making their application attractive for biomedical purposes as well as industrial inspection, non-destructive testing, material science and agro-food applications. The Gaussian THz beam however, diverges much faster than an X-ray beam and reflection and refraction effects play a dominant role, preventing the use of conventional X-ray reconstruction techniques. In this project, we focus on the development of prior-knowledge based iterative reconstruction techniques for THz tomographic data that model the physics of the THz image formation in the image reconstruction process, as opposed to performing pre- or post-processing steps. Such algorithms are nearly unexplored for THz imaging and can greatly increase the applicability of the technique through a substantial improvement in image quality.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan
• Fellow: Lumbeeck Lars-Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Many properties of food, plants or seeds that are relevant to process engineering or quality are related to microstructure. Insight in food microstructure is therefore essential to control the quality of food. In the food factory of the future, flexible and efficient processes require dedicated sensor technology and automated analysis methods. In this context, X-ray computed tomography (XCT) is gaining traction as a non-destructive method to produce extremely detailed images of both internal and external features. Current XCT based analysis of food has a number of limitations however: i) Many microstructural features of food remain invisible due to poor image contrast in soft matter. ii) Visibility and quantification of structure from absorption XCT images strongly depends on image resolution, while relevant sub-resolution size features often remain undetectable. iii) Quality control requires reliable detection and classification methods that should be compatible with process line speeds and dedicated instrumentation that is currently out of reach to the food industry. With phase contrast XCT, images can be acquired with unprecedented contrast far surpassing conventional XCT contrast. This technique was only available at large-scale synchrotron facilities, but recent developments now allow for low brilliance, polychromatic X‐ray sources in lab XCT systems. The applicability to food analysis is however to a large extent unexplored and the 3D inline application is hindered by the long acquisition time. The aim of this project is to overcome these limitations by developing novel (inline) XCT phase contrast acquisition, reconstruction and inspection algorithms specific for the food industry. This will enable us to address issues such as limited visibility of microstructural features, non-detection of sub-resolution size features and incompatibility of reliable detection and classification methods with process line speeds.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In X-ray computed tomography (XCT), X-ray images of a sample are taken from multiple angles and used to form a 3D reconstruction of the full sample, including many internal features. In a recently rising field in XCT, called phase contrast CT, a specialized set-up is used to obtain a signal that not only holds information on the absorption of the X-rays (as in traditional XCT), but also on the local scattering power in the sample and on the phase shift, a wave property. In the standard phase contrast reconstruction workflow, the acquired data is first separated in an attenuation, differential phase and dark field signal. These signals are then separately reconstructed, using an algorithm derived from traditional XCT, after which the data of the different signals is evaluated as a whole. We focus on two problems in this workflow. First, the signal separation and reconstruction use a linear model, which often does not align with reality. This model assumes a source that sends a single type of X-ray, whereas in a general setting there is a whole spectrum. Secondly, there is a relation between the different signals that are reconstructed, as they all come from the same sample. Currently this is not exploited during the reconstruction. The end goal of this project is to create a model for reconstruction exploiting all phase contrast modalities at the same time, while accounting for the different X-ray energies, such that phase contrast can be used in a quantitative setting.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan
• Fellow: Six Nathanaël

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Using X-ray Computed Tomography (XCT), internal and external characteristics of an object can be visualized in 3D in a non-destructive manner. Medical applications of XCT are well known, but also in other industrial sectors the possible applications of XCT are numerous, such as material characterisation, process and quality control and safety inspection. However, conventional XCT does not penetrate the industry very well, partly because each industrial application requires a specific XCT scan, processing speed and quality, and the XCT equipment available on the market is hardly adapted to these needs. After all, the majority of available CT equipment is very rigid in terms of scan geometry and not cost-efficient. In order to put new, innovative X-ray scanning methods into practice, imec-Visionlab purchased a custom-made X-ray device in 2019: the UniTomXL (Tescan-XRE). The alternative name provided for this device, FleXCT, emphasizes its unprecedented flexibility in terms of possible X-ray geometries for Industrial applications. Moreover, over the past 10 years, imec-Visionlab has developed the ASTRA toolbox with which the recorded X-ray scan can be reconstructed into detailed 3D images. However, in order to quickly respond to industrial XCT requests via an efficient service platform, a high-performance workflow is needed, consisting of 1) FleXCT initialization, 2) FleXCT scanning, 3) 3D image reconstruction, 4) Image visualization and analysis. In this project, the focus will therefore be on the development of such a workflow from customer demand to analysis. For this purpose, we will develop new scanning scripts, seamlessly link the ASTRA toolbox reconstruction algorithms to these scripts, and realize fast visualization and analysis of the reconstructed 3D models via the DragonFly software package (ORS, Canada, www.theobjects.com/dragonfly). Thanks to this new workflow, an efficient service platform will be offered to both academic and industrial partners.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project website

Project type(s)

• Research Project

Abstract

The project aims to improve print quality and reduce waste and cost by in-line real-time monitoring of the melt pool and the product during printing and controlling printing in-the-loop. It aims to produce the first off-axis system based on video analysis.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The major scope of this project is the evaluation of the diagnostic benefit of DFI. Therefore, the project is structured in two parts. The first track is the development of a DFI prototype for large field of view including software algorithms to reconstruct the absorption and dark field images. A second track is the clinical assessment to finally test DFI with specimen (ex- and in-vivo) to evaluate the diagnostic value of this new technique. An essential aspect of the project is the design and development of a prototype system that will be usable in clinical routine and which allows production at moderate cost. This requires fundamental changes of state-of-the-art PCI designs (hardware & software). The test-prototype which will be used for a pre-clinical trial will include these design changes to get significant evidence of the diagnostic value of the new DFI system.

Researcher(s)

• Promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Fiber reinforced polymers (F s) are increasingly used in critical components in the aerospace and automotive industry because of their low weight, strength, and cost effectiveness. Construction of F s requires an in-depth understanding of their microstructure to evaluate the strength and integrity of the composites. High resolution X-ray computed tomography has become the method of choice to investigate the composition and internal structure of F s. Unfortunately, conventional attenuation based X-ray imaging suffers from poor spatial resolution and contrast between the fibers and the polymer matrix. Fortunately, imaging methods have recently become available for lab-X-ray systems that allow to measure the local X-ray scattering (dark field imaging), leading to images with unprecedented contrast complementary to the conventional attenuation contrast. Dark field X-ray imaging is especially useful to image F s as it allows to reconstruct the full scattering profile in each voxel. However, crossing or intertwined fibers within a voxel are hard to disentangle, which makes quantification of distributions of fiber directions challenging. In this project, we will develop new models for superresolution dark field X-ray imaging that allows to quantify F fiber distributions with a subvoxel spatial resolution. This may lead to a better understanding of F properties and ultimately a better design of such materials.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In X-ray computed tomography (XCT), X-ray abso tion images of a sample are taken from multiple angles and subsequently used to form a 3D reconstruction of the full sample, based on the attenuation of X-rays. In a recently rising field in XCT, called edge illumination phase contrast CT, a specialized set-up is used to measure, apart from the attenuation, also the local scattering power in the scanned sample and the phase shift of the X-rays. Compared to attenuation, the scatter and phase signals hold complementary information of the scanned sample. Since these signals cannot be measured directly, an absorbing mask (a grating) must be placed in front of the sample and another mask in front of the x-ray detector. In the standard phase contrast imaging workflow, these masks are custom made for a specific imaging geometry and perfectly aligned to each other to achieve the right measurement conditions. The main drawback of this rigid set-up is that geometry changes that are common practice in traditional CT (e.g. zooming in on a sample to optimize the resolution and field-of-view) are not possible. Our aim here is to overcome this limitation by designing novel masks that adapt to geometry changes of the XCT set-up. This fundamental change will open up phase contrast imaging to a much larger variety of sample sizes and at different scales of resolution.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

A VLAIO COOCK project on novel applications within X-ray CT to inspect different types of materials and objects. MicroCT is a powerful, non-destructive technique for producing high quality 3D images of objects based on a set of X-ray projections. The main aim of the project is to define specific use cases that can be explored using our X-ray CT system (FLEX-CT) within an industrial setting.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Foams are found worldwide in a huge array of products, ranging from food to polyurethane foam (PU foam). However, the physics underpinning the foam formation process is not yet fully understood. A versatile and popular technique to investigate foam structure is micro X-ray computed tomography (microCT). MicroCT is a powerful, non-destructive technique for producing high quality 3D images of static objects based on a set of X-ray projections. In order to visualize the dynamics, a series of subsequent 3D images is traditionally acquired. This approach assumes the object to remain still during the acquisition of a single 3D image. However, in most dynamic imaging situations, this is only approximately valid. Therefore imaging of a fast dynamic processes such as foam formation is currently limited to synchrotron light sources as they are able to acquire a 3D image in the order of a few seconds. Unfortunately, synchrotron beamtime is very limited and experiments are typically queued for 3 to 12 months. This project will therefore focus on improving the image quality of lab-based microCT experiments of PU foam by developing, multimodal (absorption and phase data) 3D and 4D reconstruction algorithms. The key novelty lies in the use of specific prior knowledge about the foam cell shape and its material properties. On the application side my research will facilitate lab experiments and thereby greatly reduce the experiment cycle time in the industry.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan
• Fellow: Renders Jens

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Terahertz (THz) tomography is an up and coming technology that uses electromagnetic radiation with terahertz frequency for tomographic imaging. Like X-rays, THz waves can give information about the interior of an object through interaction with the object. In contrast to X-rays, there are no known negative effects of THz waves, making their application attractive for biomedical purposes as well as industrial inspection. THz rays interact with many materials in different ways. They are absorbed in polar materials such as water, penetrate most packing materials (plastic, paper, ceramics, …) and are completely reflected by metal. With terahertz tomography it is possible to, for instance visualize the contents of a sealed package. The Gaussian THz beam however, diverges much faster than an X-ray beam and reflection and refraction effects play a dominant role, preventing the use of X-ray reconstruction techniques. Here, we focus on the development of prior-knowledge based iterative reconstruction techniques for pulsed terahertz tomographic data that not only provides information on the absorption but also on phase differences.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Fellow: Lumbeeck Lars-Paul

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

There is a widespread rising need from industry to move towards 100% non-destructive inline inspection and quality control. The main challenge in X-ray based inspection is to go beyond classical X-ray radiography image processing and make the step towards fast and robust 3D inspection. This challenge is rooted in the difficulty of disentangling the 3D spatial information that is encrypted in the X-ray radiographs. imec-Vision Lab has developed methodology that can enable the introduction of high throughput inline tomography for industrial quality control. In this project we aim to push this technology past TRL4 through the development of a computationally efficient and more robust software platform, which can greatly facilitate the creation of a spin-off.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Fibrous materials are found in biology (e.g. skin, muscle, tendon, ...), but also in industry in the form of composite materials in critical components of the aerospace, automotive and building applications. Not surprisingly, there is a great demand, both clinical and industrial, for an in-depth understanding of the microstructural response of these fibrous materials to external loading parameters defining their elasticity, strength and structural integrity. In this project, a novel experimental 4D characterization toolbox based on X-ray computed tomography (XCT) will be developed, including non-invasive contrast agents and dedicated in situ measurement devices, along with advanced 4D image reconstruction and analysis methods and computational models. Two representative case studies will demonstrate the general applicability of our approach: 3D printed fibre reinforced composites and biological tissues. The proposed 4D characterization approach will allow us to gain crucial insight into the microstructural changes that occur during dynamic functional testing of both types of fibrous materials. In turn, the improved knowledge of the dynamic material behaviour can pave the way towards optimized design and production of novel 3D printed composite materials and towards a more intelligent design of next-generation solutions for tissue restoration and regeneration. The project brings together a multidisciplinary team of experts from three Belgian universities, and will facilitate the translation of the developed 4D characterization toolbox, as well as the individual methodologies, towards industry, hospitals and research centers.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Atom probe tomography is an analysis tool in materials science that allows to inspect the 3D chemical composition of needle shaped samples at the nano scale. The method works by field-induced evaporation. Ions are then consecutively emitted from the apex of the needle and are absorbed by a position sensitive detector. The result is a tomographic, atomically resolved image of the evaporated volume, represented as a point cloud in which each point is an atom. The current reconstruction approaches however were developed with homogeneous samples in mind and do not account for the complex shape of the sample surface, which evolves during the field evaporation process. The goal of this project is to develop new reconstruction methods that take the shape into account.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Terahertz radiation is non-ionizing and can be used for 3D inspection. In this project, new reconstruction methods are developed for Terahertz tomography. The THz beam is modelled and incorporated into iterative reconstruction methods.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Conventional analytical methods for CT reconstruction result in artifacts for non-ideal, constrained, CT acquisitions such as when only a limited angular range is available, or when only few projection images can be acquired due to time constraints and when the image formation model is simply inadequate. I will focus on the development of novel reconstruction techniques that take advantage of prior knowledge (e.g. sample shape, materials, energy spectra) in both X-ray absorption and quantitative phase contrast tomography to solve these issues and I will interconnect the developed algorithms with equally flexible image acquisition hardware, thereby taking advantage of prior knowledge of the object to be scanned and as well as of the imaging hardware itself.

Researcher(s)

• Promoter: De Beenhouwer Jan
• Fellow: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In X-ray computed tomography (XCT), X-ray images of a sample are taken from multiple angles and used to form a 3D reconstruction of the full sample, including many internal features. In a recently rising field in XCT, called phase contrast CT, a specialized set-up is used to obtain a signal that not only holds information on the absorption of the X-rays (as in traditional XCT), but also on the local scattering power in the sample and on the phase shift, a wave property. In the standard phase contrast reconstruction workflow, the acquired data is first separated in an attenuation, differential phase and dark field signal. These signals are then separately reconstructed, using an algorithm derived from traditional XCT, after which the data of the different signals is evaluated as a whole. We focus on two problems in this workflow. First, the signal separation and reconstruction use a linear model, which often does not align with reality. This model assumes a source that sends a single type of X-ray, whereas in a general setting there is a whole spectrum. Secondly, there is a relation between the different signals that are reconstructed, as they all come from the same sample. Currently this is not exploited during the reconstruction. The end goal of this project is to create a model for reconstruction exploiting all phase contrast modalities at the same time, while accounting for the different X-ray energies, such that phase contrast can be used in a quantitative setting.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan
• Fellow: Six Nathanaël

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

PC-CT reveals complementary information to traditional attenuation based X-ray imaging (i.e. higher contrast in soft tissue). The FleXray system will allow us to acquire data to fully explore a far wider range of applications and opportunities for PC-CT that are currently not possible: ● Exploration of advanced CT acquisition models to enable reconstruction from (1) fewer projection images and (2) projection images acquired during continuous sample rotation. This will result in faster PC-CT imaging (currently up to 8 times longer than regular CT). ● Dark field tomography is only in its infancy but recently showed huge potential in material characterisation. The FleXray system will open new research lines on dark field tomography, in particular in accurate and precise estimation of localized scattering profiles. ● Development of Krylov solvers with much faster convergence for simultaneous multimodal reconstruction of full 3D images of attenuation, phase and dark field signals.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan
• Co-promoter: Janssens Koen
• Co-promoter: Vanroose Wim

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The ASTRA toolbox is an open source platform for tomographic reconstruction. In this project, extensions for the ASTRA toolbox have been developed. These include refractive imaging such as TeraHertz tomography.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

The overarching goal of MUMMERING is to create a research tool that encompasses the wealth of new 3D imaging modalities that are surging forward for applications in materials engineering, and to create a doctoral programme that trains 15 early stage researchers (ESRs) in this tool. This is urgently needed to prevent that massive amounts of valuable tomography data ends on a virtual scrapheap. The challenge of handling and analysing terabytes of3D data is already limiting the level of scientific insight that is extracted from many data sets. With faster acquisition times and multidimensional modali-ties, these challenges will soon scale to the petabyte regime. To meet this challenge, we will create an open access, open source platform that transparently and efficiently handles the complete workflow from data acquisition, over reconstruction and segmentation to physical modelling, including temporal models, i.e. 3D "movies". We consider it essential to reach this final step without compromising scientific standards if 3D imaging is to become a pervasive research tool in the visions for Industry 4.0. The 15 ESRs will be enrolled in an intensive network-wide doctoral training programme that covers all aspects of 3D imaging and will benefit from a varied track of intersectoral secondments that will challenge and broaden their scope and approach to research.

Researcher(s)

• Promoter: Bals Sara
• Co-promoter: De Beenhouwer Jan
• Co-promoter: Sijbers Jan

Research team(s)

• Electron microscopy for materials research (EMAT)

Project type(s)

• Research Project

Abstract

Atom probe tomography (APT) is a chemical analysis technique that provides a three-dimensional atom distribution of a measured specimen. A sharpened specimen is placed into a vacuum chamber and aligned to the center of an ion detector with a high voltage bias applied between the tip and the detector. A high electric field (about > 10 V/nm) is then formed at the apex of the tip, while the atoms at the surface of the apex are ionized and the intensity of the electric field is close to the threshold of breaking atomic bonds. For analyzing low conductive materials, a continuous pulsing laser is commonly introduced as a supplement of the thermal energy which helps ions at the apex to overcome the energy barrier of evaporation. Evaporated ions are detached from the tip surface and are accelerated toward the detector according to the electric field distribution between the tip and the detector. The impact position on the detector and the travelling time, as named time-of-flight (TOF), from emission to detection are measured. It is noteworthy that, with the limited size of a detector, only those ions in the field-of-view (FOV) will reach the detector. Moreover, because of the detector efficiency, only 50-70% of the ions that reach the detector will be recorded. These effects cause significant uncertainties on determining the volume for a reconstruction. In this project we will develop novel reconstruction methods for APT.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Advanced manufacturing techniques, often based on computer aided design (CAD) models, are transforming the industrial landscape and offer exciting opportunities for producing tailor-made products with high added-value. At the same time, specifications and quality standards of end products are stringent and therefore sophisticated inspection tools are needed. In an industry 4.0 perspective, inspection occurs preferably inline to enable a rapid remediation of disturbances causing material defects and/or dimensional deviations. Hence, there is a growing demand for fast and flexible 3D metrology solutions in the factories of the future. In this context, X-ray computed tomography (CT) is gaining traction as a non-destructive method to produce extremely detailed images of both internal and external features of complex objects. However, conventional CT inspection approaches typically require many (several hundreds) X-ray projection images from a large number of viewing angles and subsequently a full 3D image reconstruction is performed. This results in a number of limitations: i) due to the lengthy acquisition and reconstruction process, CT is typically performed for offline inspections and R&D activities. Real-time inline CT scanning to achieve a 100% dimensional metrology inspection rate is not possible with the current CT systems. ii) conventional CT systems have a rigid well-defined setup, i.e. requiring either that the object can be put inside the scanner or that the source-detector system can physically rotate 360° around the object. As a result, larger objects such as a wing of an airplane or a partly assembled car cannot be scanned. iii) 3D reconstructed images may suffer from numerous artefacts (due to misalignment, beam hardening, etc.) while the traceability and uncertainty of CT measurements for metrology applications is insufficiently documented. In this project, we propose a radical paradigm shift by breaking with the traditional X-ray 3D metrology workflow through developing a new framework for 3D metrology that addresses the above mentioned problems. If successful, this SBO project will result in a flexible X-ray metrology toolkit to enable fast inline QC during production and to perform inspection tasks of larger parts. The identification of hidden defects and deviations from the nominal geometry during production will help to produce high quality products, as efficiently as possible and with a minimum of waste.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Advanced composite materials (ACMs) typically contain two or more constituents, such as resin, fibers, and pores, with different physical and chemical characteristics. When combined, they produce a material with unique properties in terms of weight, strength, stiffness, or corrosion resistance. To inspect and study their 3D internal structure in a non-destructive way, the ACMs are imaged with X-rays, after which a 3D image is reconstructed from the X-ray radiographs, and further processed and analyzed in multiple sequential steps. This conventional workflow, however, suffers from inaccurate modeling and error propagation which severely limits the accuracy with which ACM parameters of interest can be estimated. In this project, we will develop a paradigm shifting approach in which the quantification of ACM parameters is substantially improved. This will be realized in a novel workflow by 1) accounting for possible deformation of the ACM during scanning, thereby reducing image reconstruction artefacts; 2) accurately modelling all constituents of the ACM (matrix, pores, and fibers); 3) directly estimating the ACM model parameters from the X-ray radiographs, thereby preventing error propagation by providing a feedback mechanism; 4) analyzing the workflow's parameter space with respect to sensitivity and stability of parameters of interest.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In this collaboration, specific reconstruction methods are being developed for extraction of quantitative information from X-ray CT images. The methods are validated on various experimental computed tomography datasets.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

Across the food and manufacturing sectors, internal product defects or features related to local density differences (breakdown of tissues in fruit, cracks, badly glued seals,...) are often next to impossible to detect by conventional inline ('at-conveyor-belt') sensor technologies. These technologies provide only a surface evaluation (e.g., camera systems), a partial volume analysis (e.g., near infrared), 2D images of the product interior (e.g., X-ray radiography) or the chance of detection depends strongly on the viewing angle. Volumetric (3D) imaging can resolve such features and locate them in the product in a non-destructive way by means of X-ray computed tomography (CT). However, while conventional CT systems allow full 3D analysis, they are (1) too slow, (2) too expensive or (3) not adapted to inline applications. Today, the lack of adequate volumetric quality control in the agricultural industry results in high rejection rates (between 5 and 10% in some sectors), mostly after destructive random sampling, resulting in entire batches being removed from the supply chain. Moreover, it is also important to stress that the lack of volumetric 3D data impedes the automation in this sector. Economic stakes are therefore high. With iXCon we plan to establish a break-through in high throughput industrial quality control of products in the agricultural processing and manufacturing industry. We aim to achieve this by designing a prototype X-ray imaging system suitable for high-throughput inline imaging with the ability to perform full 3D volumetric analysis. Integrated analysis methodology will combine X-ray and sensor data (i.e. optical, laser, thermal) with prior knowledge (i.e. statistical shape or CAD models) to allow for fast 3D quality control of a level that is until now unachievable by the state-of-the-art methods.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project

Abstract

In summary, the main goals pursued in the scope of ADAM project are: • To develop of advanced tomographic reconstruction methods for TLGI data, generating high quality reconstructions even from a limited number of projection angles and for directly estimating the material parameters of interest • To develop data fusion techniques, combinational and comparative visualization techniques enabling data overviews and detailed inspections, as well as visual analysis techniques for AC, DPC, and DFC-data of fiber-reinforced composites including bi- and multidirectional TLGI XCT data (a specimen is scanned two or more times with different orientations in order to acquire complete refraction information in all directions). A further requirement is that these techniques need to be capable of smart handling of the large TLGI XCT dataset sizes. • To evaluate the research results and to demonstrate the developed methods in a software prototype. • To disseminate the research results and acquired knowledge in order to foster the adoption of TLGI XCT inspection in industry; providing commercialization possibilities to the industrial partners and beyond.

Researcher(s)

• Promoter: Sijbers Jan
• Co-promoter: De Beenhouwer Jan

Research team(s)

• Vision lab

Project type(s)

• Research Project