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Integrated Additive Product Development for Multi-Material Parts

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Procedia Manufacturing 2019年 33卷 3-10页

作者： Jerome Kaspar Stephan Bechtel Tobias Häfele Franziska Herter Jan Schneberger Dirk Bähre Jürgen Griebsch Hans-Georg Herrmann Michael Vielhaber Institute of Engineering Design Saarland University Campus E2 9 66123 Saarbrücken Germany Chair for Lightweight Systems Campus E3 1 66123 Saarbrücken Germany School of Engineering University of Applied Sciences Saarland Goebenstraße 40 66117 Saarbrücken Germany Institute of Production Engineering Saarland University Campus A4 2 66123 Saarbrücken Germany Fraunhofer Institute for Nondestructive Testing IZFP Campus E3 1 66123 Saarbrücken Germany

Current resource and energy requirements call for the use of most beneficial materials with suitable design at the right place as well as appropriate manufacturing technologies and associated process chains. Additive manufacturing (AM) can play a key role for prospective multi-material components, particular with its versatile advantages in design-driven flexibility, customization, and lightweight design. However, obstacles to industrialization are less due to the technology itself rather than to the actual process chain integration, from conception to production and testing. Accordingly, this contribution discusses an application example and arising problems with the integration of AM in product development. The authors highlight the most demanding steps of tailored constructive development methods, efficient manufacturing, post-processing and finishing as well as quality and lifetime management by continuous non-destructive testing.

关键词： additive manufacturing integrated product development process chain integration multi-material design efficiency

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

Modelling and Essential Control of an Oceanographic Monitoring Remotely Operated Underwater Vehicle

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IFAC-PapersOnLine 2018年第29期51卷 213-219页

作者： Rojas, Jorge Baatar, Ganzorig Cuellar, Francisco Eichhorn, Mike Glotzbach, Thomas Institute for Automation and Systems Engineering Technische Universität Ilmenau P.O.Box 10 05 65 Ilmenau98684 Germany Branch of Fraunhofer IOSB Ilmenau98693 Germany Centro de Tecnologías Avanzadas de Manufactura Pontificia Universidad Católica del Perú Lima Peru Faculty Electrical Engineering & Computer Science Aalen University Aalen Germany

Remotely Operated Vehicles (ROVs) are of great importance for a wide range of underwater missions. Although being controlled by a human operator, there is yet the desire to enable some basic autonomous behavior to relieve the operator from controlling every single movement. An accurate model is the basis for any reasonable controller design, not only to find suitable controller parameters, but also to test and optimize controllers in simulation before using them at the vehicle in reality. This paper discusses the procedures of modelling, parameter identification and the design of essential control algorithms for an existing ROV. The model considers kinematics, kinetics and thruster allocation. Two used procedures for parameter identification are presented; a third one as well was recently published in another paper. Finally, we discuss the controller design and show the simulative validation for set-point regulation and waypoint tracking. © 2018

关键词： Remotely operated underwater vehicles Actuators Controllers Electronic guidance systems Identification (control systems) Marine navigation Models Navigation Parameter estimation Propulsion Remote control Remotely operated vehicles Marine systems Marine vehicles Propulsion system simulation thrusters

EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

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arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

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arXiv 2021年

作者： Lalande, Alain Chen, Zhihao Pommier, Thibaut Decourselle, Thomas Qayyum, Abdul Salomon, Michel Ginhac, Dominique Skandarani, Youssef Boucher, Arnaud Brahim, Khawla de Bruijne, Marleen Camarasa, Robin Correia, Teresa M. Feng, Xue Girum, Kibrom B. Hennemuth, Anja Huellebrand, Markus Hussain, Raabid Ivantsits, Matthias Ma, Jun Meyer, Craig Sharma, Rishabh Shi, Jixi Tsekos, Nikolaos V. Varela, Marta Wang, Xiyue Yang, Sen Zhang, Hannu Zhang, Yichi Zhou, Yuncheng Zhuang, Xiahai Couturier, Raphael Meriaudeau, Fabrice ImViA laboratory University of Burgundy Dijon France MRI department University Hospital of Dijon Dijon France Femto-ST laboratory University of Franche-Comté Belfort France Cardiology department University Hospital of Dijon Dijon France CASIS Company Quetigny France National Engineering School of Sousse University of Sousse Sousse Tunisia LASEE laboratory National Engineering School of Monastir University of Monastir Monastir Tunisia Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Centre of Marine Sciences University of Algarve Faro Portugal School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Biomedical Engineering University of Virginia Charlottesville United States Charité – Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany German Centre for Cardiovascular Research Berlin Germany Department of Mathematics Nanjing University of Science and Technology Nanjing China Data Analysis and Intelligent Systems Lab Department of Computer Science University of Houston Houston United States Medical Robotics and Imaging Lab Department of Computer Science University of Houston Houston United States National Heart and Lung Institute Imperial College London London United Kingdom College of Computer Science Sichuan University Chengdu China College of Biomedical Engineering Sichuan University Chengdu China School of Biological Science and Medical Engineering Beihang University Beijing China School of Data Science Fudan University Shanghai China

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge’s main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures. © 2021, CC BY-NC-SA.

关键词： Cardiology

Friction properties of the head articulation in the beetle Pachnoda marginata (Coleoptera, Scarabaeidae)

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Biotribology 2019年 17卷 30-39页

作者： Barbakadze, N. Enders, S. Arzt, E. Gorb, S.N. Fraunhofer-Geselschaft Hansastraße 27c München 80686 Germany Doane College Department of Physics 1014 Boswell Ave. Crete 68333 NE United States INM- Leibniz Institute for New Materials Department of Materials Science and Engineering Saarland University Campus D2 2 Saarbrücken 66123 Germany Functional Morphology and Biomechanics Zoological Institute Kiel University Am Botanischen Garten 1-9 Kiel 24118 Germany Formerly at Max Planck Institute for Metals Research (now Max Planck Institute for Intelligent Systems) Heisenbergstraße 3 Stuttgart 70569 Germany

The head articulation of the beetle Pachnoda marginata is a micro-tribological system which consists of the ventral convex structure called gula and the corresponding concave surface of the prothorax. The surfaces of both parts are in contact and are expected to be optimised for friction reduction. The relationship between structure, mechanical properties of the cuticle material and friction properties of this micro-tribological system are investigated. The surface and material structure of the gula and its prothoracic counterpart were studied in fractured pieces of the cuticle using scanning electron microscopy (SEM). Friction force measurements and contact area estimations between the gula and a glass plate were carried out for different normal loads (0.1–10.0 mN) using two different microtribometers. The tribological behavior of the gula cuticle was studied on the (1) fresh, (2) dry, and (3) dry chemically (chloroform-methanol) treated samples. The dry samples exhibited a considerably rougher surface compared to the fresh ones. Furthermore, the chemical treatment led to some decrease in surface roughness. The fresh gula cuticle had the largest contact area and the highest friction coefficient. The drying out of the cuticle led to a decrease in both the contact area and friction coefficient. The friction coefficient was the lowest in the chemically treated gula, although the contact area was larger than in the dry condition. The tribological results were partly explained by direct measurements of the contact area fitted by the Hertz and JKR contact models. © 2019

关键词： Biotribology Cuticle Friction Insect Joint Surface

Bright, high-repetition-rate water window soft X-ray source enabled by nonlinear pulse self-compression in an antiresonant hollow-core fibre

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arXiv 2020年

作者： Gebhardt, Martin Heuermann, T. Klas, R. Liu, C. Kirsche, A. Lenski, M. Wang, Z. Gaida, C. Antonio-Lopez, J.E. Schülzgen, A. Amezcua-Correa, R. Rothhardt, J. Limpert, J. Institute of Applied Physics Abbe Center of Photonics Friedrich-Schiller-Universität Jena Albert-Einstein-Str. 15 Jena07745 Germany Helmholtz-Institute Jena Fröbelstieg 3 Jena07743 Germany CREOL College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Fraunhofer Institute for Applied Optics and Precision Engineering Albert-Einstein-Str. 7 Jena07745 Germany Active Fiber Systems GmbH Ernst-Ruska-Ring 17 Jena07745 Germany

Bright, coherent soft X-ray (SXR) radiation is essential to a variety of applications in fundamental research and life sciences. To date, a high photon flux in this spectral region can only be delivered by synchrotrons, free electron lasers or high-order harmonic generation (HHG) sources, which are driven by kHz-class repetition rate lasers with very high peak powers. Here, we establish a novel route towards powerful and easy-to-use SXR sources by presenting a compact experiment in which nonlinear pulse self-compression to the few-cycle regime is combined with phase-matched HHG in a single, helium-filled antiresonant hollow-core fibre (ARHCF). This enables the first 100 kHz-class repetition rate, table-top SXR source that delivers an application-relevant flux of 2.8×106 photons s-1 eV-1 around 300 eV. The fibre integration of temporal pulse self-compression (leading to the formation of the necessary strong-field waveforms) and pressure-controlled phase matching will allow compact, high-repetition-rate laser technology, including commercially available systems, to drive simple and cost-effective, coherent high-flux SXR sources. © 2020, CC BY.

关键词： Phase matching

Learning references with Gaussian processes in model predictive control applied to robot assisted surgery

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arXiv 2019年

作者： Matschek, Janine Gonschorek, Tim Hanses, Magnus Elkmann, Norbert Ortmeier, Frank Findeisen, Rolf Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Univ. Magdeburg Germany Software Engineering Otto-von-Guericke Univ. Magdeburg Germany Fraunhofer-Institute for Factory Operation and Automation Magdeburg Germany

One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive prediction models can be used to overcome this issue and provide predictions of these signals to the controller. In this work we propose to learn references via Gaussian processes for model predictive controllers. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator needs to follow a learned reference position based on optical tracking measurements. Copyright © 2019, The Authors. All rights reserved.

关键词： Model predictive control

Enhanced behavioral models of MEMS elements for system level verification

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Enhanced behavioral models of MEMS elements for system level...

ANALOG 2018;16th GMM/ITG-Symposium

作者： Tino Blochmann Stephan Gerth Peter Schneider Roland Jancke Fraunhofer Institute for Integrated Circuits IIS Division Engineering of Adaptive Systems EAS

ISBN: (纸本)9783800747542

This work presents an efficient approach to describe enhanced detailed mechanical behaviour of MEMS elements equivalent to a complex distributed spring mass system, which also contains weak nonlinearities on its eigenmodes. Based on an reduced order state space description of its FE mesh model the dynamic behaviour including higher eigenmodes can be simulated on system level. The core of this paper addresses the superposition of the state space model with additional suited fractional differential equations to describe stiffness variations and nonlinear behaviour in more realistic test scenarios. The discussed models are implemented in SystemC AMS - this open hardware description standard provides well suited capabilities for multi-physical domain simulations.

关键词： system level State space models behavioral models Microelectromechanical systems spring mass Elements Mass-spring systems