Over the four days we had attendees representing academia, research centres, start-up companies and large multinational corporations joining us. Once again, the training course (Day 1-2) was oversubscribed and the forum (Day 3-4) was at capacity under current restrictions for a covid safe environment. For the first time, the event was hosted by the IOP at their headquarters in London.
Prof Jean-Yves Buffiere, INSA, Université de Lyon
Image based simulation of damage development in metals under mechanical loading
Jean-Yves Buffiere graduated from Grenoble Institut National Polytechnique (INP) in 1989. After a PhD also in Grenoble (1993) and a Post Doc at Imperial College in London he was appointed Associate Professor at Institut National des Sciences Appliquées de Lyon (INSA LYON) in 1994. Since 2003 he is a Professor of Materials Science at INSA (Mechanical Engineering Department). The main focus of his research is on the characterization of damage in advanced metallic materials submitted to mechanical loading. Through collaborations with several synchrotron sources, among which the European Synchrotron Facility (ESRF) in Grenoble, he has pioneered the use of high resolution synchrotron X ray tomography for the non destructive 3D characterization of materials. He has supervised or co-supervised more than 30 PhD projects and post doctoral research programs in the field of fatigue of metals and three dimensional characterization; he is the co-author of more than a 150 papers on that topic and the co-author of a book on 3D Imaging in Mechanics of Materials (Hermes ed. ).
Dr Chu Lun Alex Leung, University College London
Seeing inside powder bed fusion with X-ray imaging and image-based modelling
Dr Chu Lun Alex Leung is a Lecturer in Imaging of Advanced Materials and Manufacturing in the Department of Mechanical Engineering at University College London (UCL). Dr. Leung and Prof. Lee are co-leading the Materials, Structure, Manufacturing group at Harwell (MSM@H), UCL, UK Leung’s research focuses on the development of intelligent advanced manufacturing using cutting-edge sensing technologies, including ultra-fast X-ray imaging, X-ray computed tomography and image-based modelling. Before his appointment, he worked as a Postdoctoral Research Fellow at UCL (2018 – 2019) and a Research Associate (2013 – 2017) in the Department of Materials at the University of Manchester, where he obtained his Ph.D. in Materials (2018). His thesis: “X-ray imaging of powder consolidation during laser additive manufacturing” led him to receive 5 individual awards, including the Sir Richard Brook prize (Best Engineering Ph.D. in the UK) sponsored by the Centre for Advanced Structural Ceramics and the Winner of the European Powder Metallurgy Association (EPMA) PM Thesis Competition 2019 sponsored by Högänas AB. Leung has also worked for British Steel (formerly known as Tata Steel Europe) and his last role was working as a New Product Development Project Manager (2011 – 2013). Besides his industrial experience, he earned a Design London fellowship (2009 – 2011) funded by Imperial College London and the Royal College of Art and worked on a product prototype using directable light technology. He received a First Class Honour degree in Aerospace Materials (MEng) and David West prize (2010) from the Department of Materials, Imperial College London.
Special Invited Speaker
Dr Tim J. Barden, Rolls-Royce plc
Sensors and sensing – Getting to the start line for imaged-based simulation
Tim Barden moved to Rolls-Royce having carried out a post-doctorial position at Bath University researching thermal methods for non-destructive evaluation. His role at Rolls-Royce primarily involves the development and introduction of new NDE technologies and is the current Industrial Chair of the Research Centre in Non-Destructive Evaluation.
Image based simulation has potential benefits to many industries. However, as with all simulation, understanding the inputs is essential and for image based simulation they are varied and each application will have its own limitations. Non-destructive evaluation (NDE) utilises a wide scope of technologies, any physical phenomena that gives information about material integrity could be used as a NDE technique. The same can be said for obtaining data for image based simulation. Modalities include the electromagnetic spectrum, such as optical and x-rays, as well as vibrational waves such as ultrasonics. The variation is further increased by the numerous techniques to stimulate a test object, monitor the response and interpret the output.
The presentation will concentrate on the more common NDE techniques used for obtaining image data including radiography, x-ray computed tomography, ultrasonic and visual. Additionally, general topics will be considered including understanding the quality of the data and reducing artefacts.
- Thomas Blumensath, University of Southampton
- Tim Burnett, The University of Manchester
- Ben Callow, Ghent University
- Guillaume Couégnat, Laboratoire des Composites Thermostructuraux (LCTS)
- Wenjia Du, University College London
- David Harman, Synopsys (Northern Europe) Ltd. UK
- Yasasween Hewavidana, Loughborough University
- Martin Jones, The Francis Crick Institute
- Muhammad Sajid Khan, WIDI WALES
- Sebastian Larsen, Imperial College London
- Fabien Leonard, The University of Manchester
- Loic Balazi Atchy Nillama, Cranfield University
- Alessandro Olivo, University College London
- Evangelos Papoutsellis, Science and Technology Facilities Council, UKRI
- Fabrice Pierron, University of Southampton
- Elena Syerko, Ecole Centrale de Nantes, Research Institute in Civil Engineering and Mechanics (GeM)
- Christopher Thornton, University College London
- Nicolas Tonello, Constelcom Ltd
- Franck Vidal, Bangor University
The majority of the presentations delivered during the workshop (days 3-4) have kindly been made available to download by the authors. Navigate below to the day and session on which the presentation was given and click on the title to download.
Mon 18 Oct 2021
- 9:30 – Registration & Coffee
- 10:00 – Session 1
Learn how to take advanced materials and develop (beginning-to-end) fully customized Deep Learning automated segmentation, such that the material is ready for preparing a simulation model (FEM mesh, pixel grid for LBM, pore network model, etc.).
- 12:30 – Lunch
- 13:30 – Session 2
ORS Dragonfly (cont.)
- 14:45 – Coffee
- 15:15 – Session 3
ORS Dragonfly (cont.)
- 17:00 – End