RELIANCE projects

<h2>Work Package 1: Instrumentation and Data Acquisition</h2>
<h3>DC1 Prototyping platform for real-time analysis for ultrafast SAXS/WAXS on fibres, composites and polymer film</h3>
<p><em>PI: Jens Wenzel Andreasen, DTU</em></p>
<h3>DC2 Quantification and digitization of composite structure using the sparse, fast imaging concept</h3>
<p><em>PI: Tomas Zikmund, CEITEC</em></p>

<h4>DC U1: Sudip Bose</h4>
<p><strong>About Sudip:</strong> I am Sudip Bose (44), currently a Marie Curie Research Fellow at the University of Manchester based out of the Henry Mosley X-ray Imaging Facility (HMXIF) at The Photon Science Institute working closely with the Henry Royce Institute on real-time imaging of FRP-Composites supervised by Prof. Philip Withers and Prof. Prasad Potluri. I am from Kolkata known as ‘The City of Joy’ from the eastern state of West Bengal, India. I hold a 1st Class B. Tech. in Ceramic Technology from Calcutta University. I secured a 1st Class Distinction in Materials Science and Engineering from The University of Sheffield thanks to the prestigious Chevening Scholarship from the Foreign and Commonwealth Development Office (FCDO) of the Government of the UK. I come with significant professional experience of approx. two decades in the field of Materials Technology covering Ceramics, Steel, Refractories, Biomaterials, and FRP-composites working with TATA Steel Ltd. - a TATA group MNC in functions e.g., operation, R&amp;D, Business Excellence, Technology and Partnership &amp; Supply chain. I completed my full-time management education (PGDM) in the strategy and operation area from XLRI School of Business and Human Resources- a premier Indian business school. I bring with me practical experience in materials manufacturing, setting up green-field plant, development and application of refractory products for steel manufacturing, and GFRP new product development. I am equipped with a whole gamut of advanced materials characterization techniques to ascertain physical and chemical properties. I am passionate about materials technology, environment, strategy, and TQM. I like playing musical instruments, painting, reading, travelling, and making new friends from different walks of life.&nbsp;</p>

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<h4>Project title: Prototyping platform for real-time depth-resolved imaging of virgin and/or recycled FRP-Composites</h4>
<p><em>PI: Prasad Potluri, University of Manchester</em></p>
<p><strong>About the project:</strong></p>
<ol>
<li>Establish an in-depth understanding of the internal structure of FRP-Composites using different imaging techniques e.g., X-ray computed tomography, etc.</li>
<li>Develop a viable concept design/geometry for a device amenable to be integrated with pultrusion for in-line imaging of composites.</li>
<li>Test the depth-resolved X-ray imaging prototype using tomography hardware at UoM and/or DTU and other commercial organizations.&nbsp;<strong><br></strong></li>
</ol>

<h4>DC S1: Carolina Gutiérrez Bolaños</h4>
<p><strong>About Carolina: </strong>I was born in Mexico City in June 1994. I receive my BSc. in Nanotechnology from the Universidad Nacional Autónoma de México (UNAM). &nbsp;Later, I pursue my studies with a Master in Materials Science from the Ludwig Maximilian’s Universitet and TU Munich. During and in between my studies I have worked in different research projects and in several facilities in Mexico and Europe, which has helped me to put in practice my knowledge and improve my capabilities for research. During my spare time, I love to sing and practice guitar, I also enjoy painting and playing volleyball.</p>

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<h4>Project title: Multi-scale characterization of carbon fiber composites using X-rays</h4>
<p><em>PI: Marianne Liebi, PSI</em></p>
<p><strong>About the project:</strong> For this project, Synchrotron X-ray scattering techniques (SAXS/WAXS) are employed to acquire state-of-the-art data from the different carbon fiber systems at the nano-, micro- and macro- structure scales. &nbsp;In particular, Small-Angle X-ray Scattering Tensor Tomography (SASTT) is employed to retrieve 3D structural and orientational information of the materials under focus. The later, does not only establish a high-resolution data set of the composites hierarchical structure, but it will provide the ground truth within the RELIANCE Network to correlate it with other characterization methods and the material’s mechanical properties. Moreover, the extensive data set, would also be employed to train digital tools to model and predict the optimal processing parameters to develop new acquisition schemes, aiming to enable a faster and accurate in-line characterization of the high-performance carbon-based products.</p>

<h2>Work Package 2: Instrumentation and Data Acquisition</h2>

<h4>DC3: Mary Chris Roperos Go&nbsp;</h4>
<p><strong>About Mary:</strong> My friends usually call me MC. I was born and raised in the Philippines. At 26 years old, I've journeyed through an academic and professional path that has taken me across the globe. It began with a bachelor’s degree in applied physics, laying the groundwork for my fascination with the sciences. Seeking to further my expertise, I pursued a master's in Image Processing and Computer Vision through the Erasmus Mundus Joint Master Degree program. This experience enriched my academic and cultural life in Hungary, Spain, and France. I then ventured to Singapore, where I served as a Research Officer at the A*Star Research Institute. In the times when I'm not coding, I like to take photos, scuba dive, and play video games.</p>

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<h4>Project title: In-line, real-time reconstruction of X-ray image data</h4>
<p><em>PI: Joost Batenburg, UL</em></p>
<p><strong>About the project:</strong> The research encapsulates a cutting-edge approach to X-ray data processing, emphasizing both immediacy and efficiency. This area of study involves the integration of optimizing the acquiring of data (polymer fibers), advance machine learning algorithms, particularly convolutional neural networks (CNNs), with traditional tomographic reconstruction methods to facilitate the real-time reconstruction of Xray images. The objective is to significantly reduce the time lag between data acquisition and image visualization, which is crucial in industrial processes. By streamlining the reconstruction process, manufacturers can quickly evaluate the materials being scanned. The research demands a solid foundation in mathematics, computer science, and engineering principles, coupled with a deep understanding of X-ray physics and digital imaging.</p>

<h4>DC4: André Ricardo Mesquita Fery Antunes</h4>
<p><strong>About André: </strong>I’m André, 25 years old, from Lisbon, Portugal. I finished both my BSc and MSc in Engineering Physics at Instituto Superior Técnico, at the University of Lisbon. There, I conducted research concerned with simulating High Harmonic Generation in crystalline solids, for about a year and a half, in the ambit of my MSc thesis’ project. My interest in research skyrocketed during that time, and I became increasingly drawn to computational science in general.</p>
<p>On a more personal level, I am rather passionate about the culinary arts, literature, and bookbinding.<strong><br></strong></p>

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<h4>Project title: Real-time quality enhancement of reconstructed X-ray scattering data</h4>
<p><em>PI: Joost Batenburg, UL</em></p>
<p><strong>About the project:</strong> This project delves into advancing small-angle X-ray scattering (SAXS) tensor tomography imaging for high performance carbon-based polymer fibres and composites. Its aim is to refine tensor tomography through deep learning methods, substantially reducing the amount of time needed for reconstruction and image processing.</p>
<p>The scope of the project includes simulating X-ray scattering processes, employing existing tools for reconstruction, harnessing deep learning for noise reduction, and optimising algorithms for faster GPU processing. The anticipated outcome is a software prototype capable of completing scattering-based reconstructions within 100 milliseconds, representing a noteworthy advancement towards real-time, in-line assessment and control of industrial manufacturing processes.</p>

<h4>DC S2: Sici Wang</h4>
<p><strong>About Sici:&nbsp;</strong>I was born in Liaoning, China, in 1995. After completing my Bachelor's degree in Optics in China in 2013, I pursued my Master's in Photonics at the University of Jena in Germany until February 2020. Then I worked as a RA within the same research group until August 2023, before joining Marianne's group. I have a keen curiosity in Mathematics and Physics, which greatly enhances my research capabilities. During my leisure time, I enjoy activities such as hiking, running, and swimming.</p>

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<h4>Project title: Computational development of X-ray scattering tensor tomography</h4>
<p><em>PI: Marianne Liebi, PSI</em></p>
<p><strong>About the project:</strong> Small angle X-ray scattering tensor tomography (SAXSTT) makes it possible to reconstruct a 3D reciprocal space map for an anisotropic sample. The project will be a continued development of iterative methods for SAXSTT reconstruction from extremely limited SAXS data that provide feedback on whether the data acquisition step has yielded sufficient information to provide reliable quantification.</p>

<h2>Work Package 3: Data Analysis and Validation</h2>

<h4>DC5: Kumari Pooja</h4>
<p><strong>About Pooja: </strong>Pooja is 27 years old PhD student from rural Bihar, India. She pursued her bachelor’s in ECE (Avionics) from IIST affiliated with ISRO, India exploring space applications using ML-based hyperspectral imaging. Later, she moved to France for a master’s in computer vision (CV) &amp; robotics from INP Grenoble. During her dissertation &amp; tenure as a research engineer at Neurospin / Inria MIND, she applied CV/ML and applied mathematics to solve biomedical applications using MRI imaging. Overall, she has acquired expertise in carrying out interdisciplinary research encompassing broad applications focused on image analysis with CV &amp; ML. In her leisure time, she finds joy in singing, cooking/baking, and practicing spiritual meditation.</p>

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<h4>Project title: Geometric Model for Ultrafast Deep Inference of X-ray Scattering</h4>
<p><em>PI: Vedrana Andersen Dahl DTU</em></p>
<p><strong>About the project:</strong> This PhD focuses on the integration of real-time data analysis and process parameters using machine learning. The main objective is to develop a learning-based geometric model that can characterize and simulate the scattering properties of anisotropic carbon-based materials. The model will be developed through nano and micro-CT imaging to ensure the model reflects the properties and variability of the materials. Through CT imaging, detected fibre will be segmented to create a parametrized surface or volume-based geometric model.</p>

<h4>DC6: Johan Friemann</h4>
<p><strong>About Johan:&nbsp;</strong>I am from Gothenburg, Sweden. I did my undergraduate studies in Engineering Physics at Chalmers university of Technology. During my physics studies I took an interest in classical mechanics and tensor calculus, which made specializing in computational mechanics a natural choice. I therefore attended the Applied Mechanics graduate program at Chalmers University of Technology, where I discovered the field of composite mechanics. After graduating I lived in Tokyo Japan for 2 years, where I worked as a simulation engineer at an AI startup us specializing in the automation of heavy construction equipment. I decided to return to academia and pursue a PhD due to a desire to hone my research and project management skills, and have the freedom to specialize in a field that interests me. My main interests are mechanical modelling, material mechanics, and composites.</p>

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<h4>Project title: DC6 Machine learning accelerated segmentation and modelling of 3D reinforced composites</h4>
<p><em>PI: Martin Fagerström, Chalmers</em></p>
<p><strong>About the project:&nbsp;</strong>Accurate prediction of the properties of 3D reinforced composites require realistic weave geometries for computational meshes. By using x-ray CT data it is possible to create realistic recreations of real composite samples. The aim of this project is to utilize machine learning tools for segmentation of CT data, and to utilize these geometries to model the mechanical properties of 3D reinforced composites. Segmentation of CT images (separating the constituent phases) through classical methods can be very challenging due to noisy data. Machine learning algorithms are promising tools, but the large quantity of data required for training makes the utilization of real CT scan data unfeasible. We therefore investigate the use of synthetic data from physically based simulation of tomography scans.</p>

<h4>DC7: Wenbo Sui</h4>
<p><strong>About Wenbo: </strong>Briefly describe (few lines) where you are from, age, education, competencies, likes.<br>I have an interesting interdisciplinary background. I got my MPhil and BSc degree in Chemistry from CUHK and HIT, focusing on electrochemistry most of the time. Then I joined the automobile industry working on simulation and state-of-health estimation of Li-ion batteries for about one year. Now I’m a PhD student at DTU and Thermo Fisher Scientific studying multimodal anomaly detection. I love guitar, badminton, and sunshine.<strong><br></strong></p>

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<h4>Project title: Fast and automated composite defect detection via an integrated multimodal digital environment</h4>
<p><em>PI: Ali Chirazi, Thermofisher Scientific FEI</em></p>
<p><strong>About the project:</strong> This project aims to achieve a fast and reliable deep learning-driven approach for quality control of carbon-based composites through multimodal material characterization input.</p>

<h2>Work Package 4: Modelling and Applications</h2>

<h4>DC8: Pinelopi Mageira</h4>
<p><strong>About Pinelopi: </strong>I come from Athens, Greece, and I am 26 years old. After achieving an Integrated Master’s degree in Mechanical Engineering with a specialization in Aerospace Engineering from National Technical University of Athens, I continued my studies, pursuing a second Master’s degree in Composite Materials and Lightweight Structures from Cranfield University. My competencies include a strong background in Composite Materials, and experience in modelling using FE, particularly Abaqus software, as well as bridging these two aspects. What I like most about this project is that it gives me the opportunity to contribute to a field that has a positive impact on society, namely wind turbines’ energy, while doing research on the field that I feel passionate about, or else Composite Materials.</p>

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<h4>Project title: Finite element analysis and predictions in 2D and 3D of the mechanical properties of in-situ 3D x-ray determined compression failure of pultruded uni‐directional composites</h4>
<p><em>PI: Lars P. Mikkelsen, DTU</em></p>
<p><strong>About the project:</strong> The project focuses on the process of predicting numerically the mechanical properties of pultruded carbon fiber composites from 3D x-ray CT scanned samples, while it aims to potentially lead to improved strength and reliability of wind turbine composite blades, as well as the elimination of critical defects, while emphasis shall be given on the simulation and prediction of the kink band, a result of fiber misalignment during compressive loading that can lead to severe mechanical property degradation. The mechanical properties in compression and fatigue will be predicted, and then compared with actual experimentally measured values, while the in-situ observed compressive failure mechanism will be reproduced. This research represents a novel approach to enhancing wind turbine blade performance, with ongoing exploration of two modeling methods.</p>

<h4>DC9: Huixin Chen</h4>
<p><strong>About Huixin: </strong>Huixin Chen, 23 years old, studied at the National University of Singapore from 2022 to 2023, where she received her master degree in material science and engineering. During that time, she has been focusing on fabrication and characterization of thermoelectric properties of carbon nanotube composites aimed at energy generation for wearable devices, laying the groundwork for current research.</p>

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<h4>Project title: In situ characterization of structural battery composites</h4>
<p><em>PI: Leif Asp, Chalmers</em></p>
<p><strong>About the project:</strong> Huixin’s research focuses on in situ characterization of structural battery composites with two particular concentrations: (1) developing structural battery aimed to provide mass-less energy storage for electrically powered structural systems and (2) in situ SAXS characterization on structural power composites, including carbon fibers and structural battery electrolyte, etc.</p>

<h4>DC10: Krisztián Hertelendy</h4>
<p><strong>About Krisztián: </strong>I come from Hungary; I am 25 years old. I have a master’s degree in Mechanical Engineering Modelling, majoring in Solid Mechanics, which I obtained at the Budapest University of Technology and Economics. I did a summer internship in 2020 at the R&amp;D-focused company Econ Engineering Ltd. based in Budapest on finite element simulation of actuated structures. I returned to the company in 2021 and worked for another two and a half years as a part-time engineer intern. Being a member of the lightweight structures group, I was involved in various projects, mainly, but not exclusively within the vehicle industry. During these years, I gained valuable industrial experience in finite element simulations of composite and metallic parts, mechanical testing, and material characterisation.</p>

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<h4>Project title: Waviness and kinking in unidirectional composites – Modelling</h4>
<p><em>PI: Ragnar Larsson, Chalmers</em></p>
<p><strong>About the project: </strong>My research aims to develop a structural-scale computational model of unidirectional composites that can accurately predict compressive strength and energy dissipation in compression. The model considers the micro-level variation of the fibre misalignment angle on the macro level through metrics such as its average and maximal value. Part of the compression tests used for model validation will be performed at secondments at Volvo Cars and The University of Manchester, and another part by fellow doctoral students in the doctoral network. The revealed effect of fibre misalignment on the initiation of kinking collapse will be mimicked via a digital twin model, and the predictions will be compared to the real-time high frame rate synchrotron X-ray imaging results.</p>

<h4>DC U2: Anirudh Kohli</h4>
<p><strong>About Anirudh:</strong> Hi, I am Anirudh Kohli. I am from India. I did my bachelor’s degree in mechanical engineering from India where I was working on plant-based biomaterials and had started and sold two of my start ups which revealed the world of technopreneurs to me. I had published around 11 articles majorly in conference proceedings and book chapters using my background of Finite element analysis and mechanical testing. Then I had an opportunity to fly to Nanyang Technological University to work on a project under NSF scheme and do my Master’s on it which was basically studying the effect of hygrothermal swelling and voids on the mechanical properties of carbon fibre reinforced polymer which exposed me to techniques of thermal, optical and spectral characterisation techniques like SEM, AFM, FTIR-ATR,DSC,TGA to name a few . As it was a multiscale project, so we tried to verify our results obtained from micromechanical models and a little bit of molecular reaction dynamics. I am foodie so like to travel and try new cuisines.&nbsp;</p>

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<h4>Project title: Waviness and kinking in 1D Composites</h4>
<p><em>PI: Phil Withers, University of Manchester</em></p>
<p><strong>About the project:</strong> This research project is focused on the quantitative analysis of fiber waviness in unidirectional composites and its influence on both compressive strength and the initiation of kinking. The study includes the development of advanced X-ray imaging tools and specialized software for tracking fiber misorientation in three dimensions. By examining the relationship between fiber waviness and kink collapse strength through extensive mechanical testing, this project aims to provide valuable insights into how manufacturing parameters affect composite performance. Real-time high frame rate synchrotron X-ray imaging will be utilized to observe and analyze composite failure mechanisms, shedding light on the role of fiber misalignment in the onset of kinking collapse.</p>

About RELIANCE

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RELIANCE

RELIANCE research projects

Work Package 1: Instrumentation and Data Acquisition

DC1 Prototyping platform for real-time analysis for ultrafast SAXS/WAXS on fibres, composites and polymer film

DC2 Quantification and digitization of composite structure using the sparse, fast imaging concept

DC U1 Prototyping platform for real-time depth-resolved imaging of virgin and/or recycled FRP-Composites

DC S1 Multi-scale characterization of carbon fiber composites using X-rays

Work Package 2: Instrumentation and Data Acquisition

DC3 In-line, real-time reconstruction of X-ray image data

DC4 Real-time quality enhancement of reconstructed X-ray scattering data

DC S2 Computational development of X-ray scattering tensor tomography

Work Package 3: Data Analysis and Validation

DC5 Geometric model for ultrafast deep inference of X-ray scattering

DC6 Machine learning accelerated segmentation and modelling of 3D reinforced composites

DC7 Fast and automated composite defect detection via an integrated multimodal digital environment

Work Package 4: Modelling and Applications

DC8 3D X-ray imaging and scattering techniques for advanced material characterization of pultruded carbon fibre composites for wind turbine blades

DC9 In situ characterization of structural battery composites

DC10 Waviness and kinking in unidirectional composites – Modelling

DC U2 Waviness and kinking in 1D Composites