Advanced Training Courses (Certificate Program) 2025
Learning Contents | June 02-04, 2025
The advanced training is highly valuable for both experts and newcomers in the field of 3D printing. An introduction to 3D printing offers comprehensive insights into current technologies, methods, and applications, enabling individuals to stay updated with the latest advancements. Additionally, tutorials on digitization and data preprocessing shed light on the crucial steps required in industry and biomedicine to ensure accurate and efficient 3D printing processes. For professionals in additive manufacturing, a tutorial on industrial X-ray CT provides fundamental knowledge and practical applications for evaluating parts. In addition, health economic topics related to AM are also covered in order to meet the challenges of rising costs in the healthcare system.. Lastly, hands-on tutorials on 3D manufacturing and non-destructive testing with computed tomography (CT) provide practical experience, allowing individuals to apply their knowledge in real-world scenarios. Overall, these tutorials empower individuals to expand their expertise, enhance their skills, and contribute to the advancement of the 3D printing field.
Participation in the advanced training courses requires a separate registration. Modules may be changed at short notice without compromising the quality of the training.
Module Descriptions
Introduction to 3D Printing: Current Technologies, Methods and Applications
Dr. Thomas Friedrich, Department Head, Medical Additive Manufacturing, Fraunhofer IMTE, Germany
Course Description: The goal of the course is to provide attendees with a comprehensive overview of 3D printing, covering the various technologies, methods, and different applications in various industries. The course begins with an overview of the basic principles of 3D printing. Attendees will gain an understanding of the additive manufacturing process, in which three-dimensional objects are produced by sequentially depositing layers of material. The major components of a typical 3D printing system will be discussed, including printers, materials and software. Current 3D printing technologies will then be introduced.
Course Outline: Attendees will be introduced to common techniques such as fused deposition modeling (FDM), stereolithography (SLA), selective laser sintering (SLS), polyjet printing (PJP) and selective laser melting (SLM). The operating principles, advantages, limitations, and appropriate applications of each technology will be discussed, so that attendees will be able to make informed decisions in selecting the appropriate technology for their specific needs and place the following specific modules in context.
Course Duration
Total 1 hour
Learning Objectives
- Basic methods and materials of common 3D printing technologies
- Terminology of additive manufacturing
- Choice of methods and limitations
- Exemplary applications
Target Group
This module is shaped for technical and scientific personnel, post-grad students, postdocs, and industry professionals who would like to learn the basics of additive manufacturing, or already have some experience and want to broaden their knowledge across the wide technological range.
Lecturer
Dr. Thomas Friedrich received his Diplom Physiker degree in 2008 and his Dr. rer. nat. degree of applied Physics in 2014 from the University of Bayreuth, Germany. He worked in the field of magnetohydrodynamics in ferrofluids and studied the transport and pattern formation in colloidal suspensions of magnetic nanoparticles. Between 2015 and 2020 he was developing components for magnetic medical imaging systems and therapeutic applications at the institute of medical engineering at the University of Lübeck. Since 2020, Thomas is the Department Head of Additive Manufacturing at Fraunhofer IMTE.
Digitization and Data Preprocessing in Industry and Biomedicine
Laura Hellwege, University of Lübeck and Fraunhofer IMTE, Germany
Course Description: One of the strengths of additive manufacturing is its comparatively low price for one-off or low volume production. It is therefore highly interesting for prototype development or individualized applications. For example, spare parts for old machines - whose CAD model is no longer available - can be produced or prostheses for patients can be individualized. In order to generate a corresponding print template, technologies are required that scan a real object and make it available in a processable digital data structure. Optical or tomogram-based methods from quality control, reverse engineering or medicine come into play here. Defective spare parts can be digitized by them and their defects repaired or, as it were, prostheses can be adapted to the anatomy of a patient. Polygon meshes extracted from tomographic images have a very high resolution, for which some printing programs are not designed. Data sources such as optical scanners only provide point clouds that must first be converted into a surface mesh. In addition, the measurement processes carried out are subject to errors. Classically designed CAD models, on the other hand, are precise and mostly made up of a few macroscopic and geometric shapes. Converting measurement data into printable polygon grids therefore requires data pre-processing. A trend in CAD design is the optimization of components which, for example, should have a better weight/load ratio. This usually results in rather organic geometries whose polygon meshes resemble anatomical structures. In individualized medicine, the adaptation of implants to digitized anatomical structures also requires an understanding of both topics in order to avoid sources of error due to unsuitable data filters. In addition to the two examples listed, many other applications are conceivable. This course is intended to provide a condensed insight into the topic so that attendees can identify possible areas of application in their field and create a strategy for implementation based on the methods presented.
Course Outline: In a thematic introduction, processes for digitization are presented and characterized so that suitable processes can be evaluated for the requirements in one's own work area. In addition, the structure and properties of printable polygon gratings and their embedding in different file formats will be presented. Together with the requirements for printable polygon meshes defined in the module, the foundations for the more in-depth modules are prepared.
Modalities that can convert a real object into a digital model range from manual construction to fully automated scanning using methods such as computed tomography, magnetic resonance tomography, and laser-based optical methods. The operation of these methods and procedures for raw data processing are explained. Algorithms that overcome the discrepancy between the measured polygon mesh and the polygon mesh that serves as a print template are highlighted. Causes for possible error sources are shown by practical examples.
Finally, an insight into a new area of machine learning and its influence on the future creation of print templates is given.
Course Duration
Total 1 hour
Learning Objectives
- The attendees are familiar with techniques for digitizing real objects and can assess which methods might be suitable for their application examples.
- The attendees can name advantages and disadvantages of the different digitization techniques.
- Attendees have an overview of frequently occurring artifacts and know algorithms and programs that regulate them.
- Attendees have gained first practical experience in digitizing real objects and post-processing their digital representation.
Target Group
Attendees from pre-development, research and the field of individualized medicine as well as from design, with an interest in theoretical background knowledge and practical application - basic knowledge from the field of additive manufacturing is advantageous for this course.
Lecturer
Laura Hellwege was born in Stade, Germany in 1997. She received her Master of Science in Mathematics in Medicine and Life Sciences in 2020 from the Universität zu Lübeck, Germany. During her study she was mainly interested in numerical methods and inverse problems in medical image processing. In 2020 she wrote her master thesis at the Institute for Medical Engineering about the usage of Neural Networks for preprocessing in Computed Tomography. Since 09/2020 she is a Research Assistant at the Institute of Medical Engineering where she works in the field of computer tomographic reconstruction. Currently, Laura is member of the CT Research Team at Fraunhofer IMTE.
Printed Anatomy: Brain Artery Phantoms - Essential Data Processing and Printing Strategies
Dr. med. Andreas Stroth, Neuroradiology, UKSH Lübeck, Germany
Course Description: The 3D printing of arteries possesses unique characteristics within the field of medical 3D printing. While most human organs can be replicated solely based on their external surface using medical imaging, arteries necessitate the inclusion of their internal surface. This requirement is particularly critical for brain arteries, where achieving an accurate anatomical replication is crucial due to the intricate nature of the anatomical structures involved. Even slight discrepancies during the replication process can result in significant disparities between the original structure and the 3D printed target structure. Furthermore, it is essential to consider the substantial anatomical differences between brain arteries and arteries in other parts of the body. In the planning phase of a brain artery replication project, it is crucial to critically evaluate the intended purpose of the 3D printed replica. By focusing on the specific application, it becomes possible to meaningfully constrain the multiple degrees of freedom associated with 3D printing brain arteries. This approach not only conserves important resources, including time and the necessary software and hardware but also ensures that the resulting replicas are tailored to their intended uses.
Course Outline: The aim of this course is to provide participants with the essential fundamentals in medical 3D printing of brain arteries. This includes presenting both anatomical basics and decision-making guidance for selecting the required medical imaging methods. Important intricacies in the preprocessing of anatomical data of brain arteries will be explained, and the fundamental strategies in planning and manufacturing a 3D printed brain artery phantom will be presented.
Course Duration
Total 1 hour
Learning Objectives
- Important characteristics in medical 3D printing of arteries, particularly brain arteries
- Fundamental considerations regarding different types of 3D printed brain artery phantoms
- Key steps in data pre-processing and post-processing
- Basic printing methods suitable for replicating brain arteries
Target Group
Scientists, technical staff, and physicians who are interested in medical 3D printing of arteries, particularly brain arteries, and already have some experience with medical 3D printing. A basic understanding of imaging techniques, anatomical segmentations, and the application of CAD in a medical context is advantageous but not necessary for the course.
Lecturer
Dr. med. Andreas Stroth is a board-certified radiologist and holds the European Diploma in Radiology (EDiR). He completed his medical studies at Heinrich Heine University in Düsseldorf, followed by a residency in radiology at the University Medical Center Schleswig-Holstein (UKSH), Lübeck Campus. He is currently working as a specialist in interventional neuroradiology at UKSH. During his doctoral research, he focused intensively on the endovascular recanalization of arteries. Since 2023, he has also been contributing his expertise at the Laboratory for Experimental Neuroradiology at UKSH, where he is actively involved in advancing the development of this field.
3D Printing of Intracranial Aneurysm Flow Models: A Practical Guide
Dr.-Ing. Mariya Pravdivtseva, Section of Biomedical Imaging, Department of Radiology and Neuroradiology, University Medical Center Schleswig Holstein, Campus Kiel, Germany
Course Description: Intracranial aneurysms are localized bulges in blood vessels caused by a weakened vessel wall. If an aneurysm ruptures, it can lead to life-threatening conditions. These aneurysms can be treated using minimally invasive endovascular devices such as coils, flow-diverting stents, and intrasaccular devices. While many aneurysms are effectively treated, complications such as the occlusion of adjacent branches - potentially leading to a stroke - or insufficient flow reduction, which may result in delayed aneurysm rupture, can still occur. Therefore, improving aneurysm treatments is crucial to enhancing patient outcomes. However, developing and testing new devices is costly, time-intensive, and heavily reliant on animal studies. This course introduces the use of 3D-printed vascular flow models as a cost-effective and scalable platform for evaluating novel aneurysm treatments. These models are geometrically accurate, replicate flow patterns observed in vivo, and enable proof-of-concept testing of endovascular devices, potentially reducing the need for animal trials. Additionally, the ability to create patient-specific vascular models in a clinical setting opens opportunities for education and training in endovascular treatments. Participants will learn how to design, construct, and optimize 3D-printed vascular models, identify and correct common errors, and use these models to assess the efficacy of aneurysm treatments. If you are a medical professional looking to establish an in-house 3D printing lab, an engineer developing endovascular devices, or a researcher seeking a realistic platform for imaging or flow experiments, this course is for you. By the end of the course, participants will gain the knowledge and resources - including a detailed manual using only open-source software - to create and apply patient-specific vascular models.
Course Outline: 1) A short introduction to intracranial aneurysms – An overview of intracranial aneurysms and endovascular treatment methods, including flow-diverting stents and intrasaccular devices. 2) Workflow for aneurysm model design and fabrication – A step-by-step process, including segmentation, artifact correction (e.g., closing holes, smoothing, vessel inflation), removal of vascular branches, wall creation, addition of connectors and imaging markers, and 3D printing. 3) Overview of techniques for geometric quality evaluation of 3D-printed vascular models – Hausdorff difference mapping, aneurysm and vessel dimension analysis, and insights into why accuracy matters for treatment testing. 4) Placement of endovascular devices – Practical considerations for implant placement (both commercial and novel) within models, including flow setups and deployment strategies. 5) Imaging modalities for testing endovascular treatment efficiency – Introduction to imaging techniques such as 4D flow MRI, particle image velocimetry (PIV), and digital subtraction angiography (DSA). 6) Preparing vascular models for imaging and testing – Critical preparation steps for optimizing models for specific imaging modalities and flow experiments. 7) Data analysis and next steps – Common strategies for evaluating treatment efficiency based on imaging results and recommendations for future experiments.
Course Duration
Lecture: 2 hours
Learning Objectives
- Understand the key steps to design patient-specific intracranial aneurysm models and how to address common errors.
- Learn quality metrics for validating the geometric accuracy of the produced model.
- Gain insights into preparing aneurysm models for endovascular treatment testing using imaging modalities such as 4D flow MRI, PIV, and DSA.
- Explore strategies for evaluating device efficiency using physical and imaging-based methods.
Target Group
1) Medical professionals interested in establishing 3D printing labs for procedural practice, research, and teaching. 2) Engineers and Material Scientists developing endovascular implants who require an affordable, realistic platform for device testing. 3) Physicists and Imaging Experts improving imaging modalities to assess aneurysm treatments who need of realistic and reproducible vascular flow models. 4) General Audience interested in the medical applications of 3D printing.
Lecturer
Dr.-Ing. Mariya S. Pravdivtseva is a medical engineer with expertise in 3D printing, rapid prototyping, and imaging techniques such as flow-sensitive MRI and X-ray imaging. She leads the preclinical vascular research group at the University Medical Center Schleswig-Holstein, Kiel, where she focuses on advancing the diagnosis and treatment of vascular pathologies. Dr. Pravdivtseva’s work has been recognized with several awards, including one from the Association of Patients with Aneurysms for developing patient-specific aneurysm models and the W.S. Moore Award from the International Society of Magnetic Resonance in Medicine for translating basic research into medical applications.
Metal Binder Jetting for Medicine: experimental, simulation, digitalization, applications
Dr.-Ing. Elena Lopez, Head of Division Additive Manufacturing Fraunhofer IWS, Germany
Course Description: Metal additive manufacturing has so far focused on laser-based processes (LPBF or DED i.e.). These are characterized by a high technical maturity (Technology Readiness Level, TRL), but do not meet all the requirements in terms of materials, geometries and productivity. The industry is therefore increasingly focusing on sinter-based additive manufacturing (SBAM) like Binder Jetting. These offer advantages such as processing materials that are difficult to weld, high productivity or high surface quality. In addition to the expectations and benefits, the industry has reservations about the achievable properties due to the lower TRL. Industry adaption of this technology is especially challenged with regard to the achievable properties such as near-net-shape and material structures, as here experimental know-how and trial & error were often the only knowledge available. On the one hand, there is a need for digital prediction of the sintering shrinkage of complex structures and in the adjustment of material properties on the other hand.
Course Outline: This course will provide insights in the experimental work with Binder Jetting, will present current results concerning digitalization, process monitoring and simulation efforts related to this technology field and will illustrate some interesting applications, especially in the field of medicine.
Course Duration
Total 2 hours
Learning Objectives
The module aims to provide a deep understanding of the Binder Jetting process compared to traditional laser-based additive manufacturing methods. Participants will learn about experimental techniques, process monitoring, and the digitalization and simulation efforts specific to this technology. The course also explores the practical applications of Binder Jetting in the medical field, addressing specific manufacturing needs. Additionally, it discusses industry challenges and solutions to enhance the adoption and effectiveness of this technology in practical settings.
Target Group
Attendees from engineering, medicine and research, with an interest in practical background knowledge and application. Basic knowledge from the field of additive manufacturing is advantageous for this course.
Lecturer
Dr.-Ing. Elena Lopez studied chemical engineering at the Universidad de Valladolid in Spain and Friedrich-Alexander-Universität Erlangen-Nürnberg in Germany. She finished her PhD thesis on the topic of plasmachemical etching of silicon solar wafers at the Technische Universitaet Dresden. She is Head of Department for Additive Manufacturing at the Additive Manufacturing Center Dresden (AMCD) at Fraunhofer IWS and teaches AM as an adjunct professor. She also represents Women in 3D Printing as Regional Director Europe.
When AM meets AI: Machine Learning for Shape Estimation and Manipulation
Prof. Dr. Jannis Hagenah, Digital and Robotic Surgery, University Medical Center Göttingen, Germany
Course Description: One challenge of applying AM in the medical domain is that the desired, optimal shape of the object of interest is not always known. This is for example the case in personalized protheses, where only a pathologically deformed state can be assessed using medical imaging, which is obviously not the one we want to manufacture. The course module "When AM Meets AI: Machine Learning for Shape Estimation and Manipulation" explores the fusion of additive manufacturing (AM) and artificial intelligence (AI) in the field of medicine to overcome these shape uncertainties by leveraging the predictive power of data-driven models. Through a combination of theoretical knowledge and practical case studies, participants will gain valuable insights into leveraging ML techniques to optimize and enhance the design, production, and customization of medical objects using additive manufacturing technologies.
Course Outline: The course begins with an introduction to the challenges of AM technologies in medicine regarding uncertain or unknown geometries, motivating the need for shape prediction and manipulation. The fundamentals of ML for shape estimation are covered, including preprocessing, feature extraction, and training and evaluation of ML models. Statistical shape modelling is emphasized as a key technique within this context. Participants then explore ML techniques for shape manipulation in AM, utilizing geometric deep learning methods and examining case studies related to personalized implant or prosthesis development. A significant aspect of the module is addressing shape estimation challenges in medical scenarios where the shape is unknown or uncertain, such as in the presence of diseases or imaging limitations. Participants learn about ML-based approaches to overcome these challenges and estimate shapes accurately. The module concludes with a look at future directions and challenges in the convergence of AM and AI, including emerging trends, ethical considerations, and potential advancements in shape estimation beyond current limitations.
Course Duration
Total 1 hour
Learning Objectives
- Understand the domain-specific challenges of shape estimation for additive manufacturing (AM) in medicine
- Gain insights into the role of artificial intelligence (AI) in enhancing AM processes
- Acquire knowledge of machine learning (ML) algorithms for shape estimation in medical contexts
- Explore the integration of AM and AI for optimizing and customizing medical object design and production
Target Group
This course module is designed for researchers, engineers, and medical professionals with a background in additive manufacturing and a keen interest in leveraging artificial intelligence techniques for shape estimation and manipulation. Participants should have prior knowledge of AM technologies. Familiarity with basic machine learning concepts will be beneficial but not mandatory.
Lecturer
Prof. Dr. Jannis Hagenah is Professor for Digital and Robotic Surgery at the University Medical Center Göttingen in Germany since 2024. He is a researcher on Artificial Intelligence in Medicine with strong interest in medical image and signal processing, medical robotics, and continual machine learning. In 2023, he joined Fraunhofer IMTE where he leads the research on Surgical AI. Before that, he worked at the University of Oxford and the University of Lübeck. He holds a PhD in computer science and an M.Sc. in medical engineering science, both from University of Lübeck. Furthermore, he is lecturer at the London Metropolitan University and the University of Applied Sciences Vienna. Jannis is board member of the Medical Imaging with Deep Learning (MIDL) foundation and treasurer of the IEEE Engineering in Medicine and Biology (EMBS) Germany Chapter.
Post-Processing in Practice
Stefan Ritt, Chairman of international committee, ambassador AMUG (Additive Manufacturing Users Group)
Course Description: Today, various 3D-printing methods and machines have entered the production producing series parts from different materials like e.g. plastics, metals, ceramics and also bio-materials. After designing the parts in CAD and converting the files as well as printing the parts with the different technologies, usually the part coming out of the 3D-printer does need significant postprocessing before it can be used in practise. This could be washing, cleaning, brushing, cutting, machining, grinding, optical and/or chemical hardening, etching and polishing. Furthermore, heat treatment or bio reactors and sterilisation might be the choice depending on material and use of parts. Finally, a nondistructive or distructive quality testing and assurance process is usually in place to safeguard to propper build and quality of the final part.
Course Outline: This module will present and describe the various steps to be taken until the final part can be used. This way the audience will receive valuable information and guidelines to establish, modify and/or complete the manufacturing process chain in their own work environment. Furthermore, the module will enable open discussion and exchange of information with practicioners having valuable and international first hand experience in the field.
Course Duration
Total 2 hours
Learning Objectives
- Most common types of support structures and how to remove them
Target Group
Technical and scientific personnel with an interest in professional additive manufacturing with little or no practical experience, or some experience with entry level 3d-printing. Basic insight into CAD and construction is advantageous but not necessary for the course.
Lecturer
Stefan Ritt comes from Lübeck and has worked as an international engineer for 38 years after serving in the German army and studying physical engineering. His professional career has always taken him to companies focussing on international markets and increasingly to management positions. After technical development of devices and management of quality assurance for a medium-sized manufacturer of professional beverage and vending machines, Stefan Ritt worked in the EU-wide product management of an electronics manufacturer. He spent many years building up international sales and marketing for SLM Solutions Group AG and was also involved in the very successful IPO in 2014. He then founded the European sales and service office for an Australian company. 3D printing and additive manufacturing were and still are the focus of his knowledge and work in over 60 countries. He is head of the international committee of the world's largest user association for 3D printing, AMUG, based in the USA, a member of the EPMA-AM Board for powder metallurgy in Paris and has actively promoted technical standardisation in this area. In this role, he also represented DIN as the contact person for aviation standardisation in China for several years. In addition to regular guest lectures on supply chain management at the Lübeck University of Applied Sciences in the international business faculty and mentoring start-ups in an accelerator, Stefan Ritt became involved in the German Aerospace Industries Association (BDLI) at an early stage.
Image Segmentation, Data Processing, and 3D Printing in Surgical Planning
Prof. Dr. Jan Wolff, PhD, Clinical Advisor, Fraunhofer IMTE, Germany and UKSH, Germany
Course Description: Advancements in medical imaging and 3D printing have revolutionized surgical planning by enabling precise, patient-specific models. Image segmentation and data processing are essential for extracting accurate anatomical structures from medical scans, ensuring high-quality visualization for preoperative assessments. The accuracy of segmentation directly impacts 3D-printed models used for surgical simulations, patient education, and implant design. This course covers segmentation techniques, data processing workflows, and 3D printing technologies to optimize surgical outcomes.
Course Outline: Participants will gain fundamental and advanced knowledge in: 1) Imaging modalities (CT, MRI) for 3D printing; 2) Segmentation techniques for various anatomical structures; 3) Data preprocessing and post-processing strategies; 4) Software tools for segmentation and 3D modeling; 5)Surgical planning with 3D-printed models
Course Duration
Total 2 hours
Learning Objectives
- Understand the role of image segmentation in surgical planning and 3D printing
- Learn key segmentation and data processing techniques
- Identify suitable 3D printing technologies for surgical applications
- Analyze case studies of successful surgical planning using 3D-printed models.
Target Group
This course is designed for healthcare professionals, biomedical engineers, and researchers interested in medical imaging, surgical planning, and 3D printing. Prior experience in image segmentation, CAD modeling, or medical 3D printing is helpful but not required.
Lecturer
Jan Wolff is Professor and currently a Senior Researcher and Surgeon at UKSH and a Clinical Advisor at Fraunhofer IMTE, specializing in the clinical applications of medical technologies. Previously, he served as:
- Full Professor and Head of the Section of Oral and Maxillofacial Surgery & Oral Pathology at Aarhus University
- Head of the Medical Center at Fraunhofer IAPT, focusing on additive manufacturing for medical applications
- Senior Researcher at Universitätsklinikum Hamburg-Eppendorf (UKE), specializing in clinical applications of 3D printing
- Assistant Professor at Amsterdam UMC, specializing in regenerative medicine, tissue engineering, and medical 3D printing
- Senior Researcher in the Adult Stem Cell Group at Tampere University, focusing on regenerative medicine, tissue engineering, and medical 3D printing
- Senior Surgeon at Tampere University Hospital, working on the clinical applications of stem cells for the reconstruction of oral and maxillofacial structures
His research focuses on regenerative medicine, stem cell therapy, 3D printing in medical applications, and artificial intelligence. He has received numerous awards, published extensively, and is an active member of professional societies.
Lab Course - Industrial X-ray CT: Fundamentals and Applications in the Evaluation of Additive Manufacturing Parts
Laura Hellwege, University of Lübeck and Fraunhofer IMTE, Germany
Course Description: Additive Manufacturing (AM) has developed strongly in the last decade, making it possible today to manufacture end-use products in different industries, including the medical field. Despite the numerous advantages offered by AM technology, there are hundreds of parameters that can reduce the quality of produced parts, necessitating stringent quality control. The quality and consistency of AM parts can be influenced by many interrelated factors, including design, material properties, and manufacturing parameters, which can result in defects and deviations from the desired design specifications, varying in severity and influence on part performance. One of the primary challenges associated with AM is ensuring the quality and consistency of produced parts, particularly internal defects like voids, cracks, and inclusions. These defects can lead to reduced mechanical properties, lower fatigue life, and premature failure of the parts. To ensure the quality and reliability of AM parts, it is crucial to conduct a comprehensive inspection and analysis. This is where X-ray Computed Tomography (CT) systems come into play. X-ray CT provides non-destructive high-resolution imaging of AM parts, allowing for detailed analysis of the part's internal structure and defects. Moreover, X-ray CT enables early identification of defects and deviations from the design specifications in the production process, improving quality control and reducing waste and rework.
This training workshop will provide participants with the necessary knowledge and skills to utilize X-ray CT systems for evaluating AM parts and improving the quality control of the production process.
Course Outline: The proposed training course is a 3-hour workshop designed for post-graduate (Master’s and Ph.D.) and postdoc students interested in utilizing X-ray Computed Tomography (CT) for evaluating additive manufacturing (AM) parts. The course comprises a mix of lecture slides, a practical CT session, and hands-on training in analyzing CT data using Dragonfly software, with a particular emphasis on deep learning. The theoretical section will cover the fundamental principles of industrial X-ray CT, encompassing the different types of X-ray sources, detectors, and imaging geometries, as well as the advantages and limitations of X-ray CT imaging. The practical session where we set up scans and selected optimal scanning parameters for an onsite Comet Yxlon CT system. The course instructor will offer guidance on optimizing scan parameters to obtain high-quality images of the samples. Participants will be introduced to Dragonfly software, a robust X-ray CT data analysis tool, in the data analysis section. Specifically, participants will learn how to use Dragonfly for porosity analysis, deep learning segmentation, and related tools. The course instructors will provide practical experience and guidance on using these tools effectively. The training organizers will provide the necessary equipment, including an X-ray CT system, computers with Dragonfly software, and sample preparation tools.
Course Duration
Total 2 hours; 2 Submodules:
1) Basic introduction to X-ray CT
2) Practical session: scanning AM parts - Hands-on CT
Learning Objectives
By the end of the training, participants will:
- Understand the basic principles of industrial X-ray CT and its applications
- Be able to set up scans and choose optimal scanning parameters for their own samples
- Know how to use Dragonfly software for porosity analysis, deep learning segmentation, and related tools
Target Group
This training session is a great opportunity for post-grad students and postdocs to learn about industrial X-ray CT and data analysis using Dragonfly software. The practical session and sample scanning will provide hands-on experience and allow participants to apply their knowledge. We encourage interested individuals to register for this training and take the first step towards becoming experts in industrial X-ray CT and data analysis.
Lecturer
Laura Hellwege was born in Stade, Germany in 1997. She received her Master of Science in Mathematics in Medicine and Life Sciences in 2020 from the Universität zu Lübeck, Germany. During her study she was mainly interested in numerical methods and inverse problems in medical image processing. In 2020 she wrote her master thesis at the Institute for Medical Engineering about the usage of Neural Networks for preprocessing in Computed Tomography. Since 09/2020 she is a Research Assistant at the Institute of Medical Engineering where she works in the field of computer tomographic reconstruction. Currently, Laura is member of the CT Research Team at Fraunhofer IMTE.
Lab Course - 3D Generative Manufacturing and Post-Processing
Dr. Thomas Friedrich, Department Head, Medical Additive Manufacturing, Fraunhofer IMTE, Germany
Course Description: Introducing a new 3D printing process in a productive environment comes with a steep learning curve to climb. This hands-on course provides a basis, which will allow the participants to evaluate devices, materials and tools to efficiently establish professional additive manufacturing with polymers. The goal of the course is to give attendees a practical insight, to help them understand how soft- and hardware features and different materials as well as technical specifications of devices translate into the daily work with professional polymer 3D printing equipment.
Course Outline: The course covers the complete workflow from the import of 3D models into the printer software, via setting up print parameters and part placement to support material removal, postprocessing and some insights into exemplary machine maintenance.
Course Duration
Total 2 hours; 2 Submodules
1) Printjob preparation
2) Postprocessing and machine maintenance
Learning Objectives
- Most relevant Parameters for FDM and MJP
- Considerations and implications on part placement
- Most common types of support structures and how to remove them
- Critical components of common printers and insight into machine maintenance
Target Group
Technical and scientific personnel with an interest in professional additive manufacturing with little or no practical experience, or some experience with entry level 3d-printing. Basic insight into CAD and construction is advantageous but not necessary for the course.
Lecturer
Dr. Thomas Friedrich received his Diplom Physiker degree in 2008 and his Dr. rer. nat. degree of applied Physics in 2014 from the University of Bayreuth, Germany. He worked in the field of magnetohydrodynamics in ferrofluids and studied the transport and pattern formation in colloidal suspensions of magnetic nanoparticles. Between 2015 and 2020 he was developing components for magnetic medical imaging systems and therapeutic applications at the institute of medical engineering at the University of Lübeck. Since 2020, Thomas is the Department Head of Additive Manufacturing at Fraunhofer IMTE.
Lecturers
Prof. Dr. Thorsten Buzug
Fraunhofer IMTE, Germany
Dr. Thomas Friedrich
Fraunhofer IMTE, Germany
Laura Hellwege
University of Lübeck and Fraunhofer IMTE, Germany
Dr.-Ing. Mariya S. Pravdivtseva
UKSH Campus Kiel, Germany
Prof. Dr. Jannis Hagenah
University Medical Center Göttingen, Germany
Dr. med. Andreas Stroth
Neuroradiology, UKSH Lübeck, Germany
Prof. Dr. Jan Wolff
Fraunhofer IMTE and UKSH, Germany
Dr.-Ing. Elena Lopez
Fraunhofer IWS, Germany
Stefan Ritt
AMUG (Additive Manufacturing Users Group)