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International Electric Drives Production Conference (EDPC)

作者： Maik Linnemann Mirko Bach Verena Psyk Markus Werner Marcel Gerlach Nadine Schubert Fraunhofer- Institute of Machine Tools and Forming Technologies Chemnitz Germany

ISBN: (数字)9781728143194

ISBN: (纸本)9781728143200

Due to new requirements regarding the efficiency of electrical machines it is necessary to increase the slot filling factor of such machines. forming technology offers an excellent opportunity to achieve this aim. The paper therefore presents a method for flexibly adapting the cross-sectional shape of the coil to the geometry of the slot. First, the potentials and limits of forming technologies are presented. Furthermore, the forming tool required for the cross-sectional adaptation is described. Advantages and limits of the method are analyzed and evaluated. In particular, factors such as elongation and burr formation play a decisive role here. Finally, an outlook is given and possible research approaches are discussed.

关键词： Wires Filling Windings Shape Pressing tools Presses

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Guideline for Deployment of machine Learning Models for Predictive Quality in Production

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Procedia CIRP 2022年 107卷 815-820页

作者： Henrik Heymann Alexander D. Kies Maik Frye Robert H. Schmitt Andrés Boza Fraunhofer Institute for Production Technology IPT Steinbachstr. 17 Aachen 52074 Germany Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University Campus-Boulevard 30 Aachen 52074 Germany Centro de Investigación Gestión e Ingeniería de la Producción (CIGIP) Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain

Predicting product quality represents a common area of application of machine learning (ML) in manufacturing. However, manifold challenges occur during the integration of ML models into production processes. Therefore, this paper aims to provide a guideline for the deployment of ML models in production environments. Relevant decisions and steps for deploying models in predictive quality use cases are demonstrated. The results for each component of the proposed guideline - deployment design, productionizing & testing, monitoring, and retraining - have been validated with industry experts including exemplary implementations.

关键词： Artificial Intelligence machine Learning Deployment Production Manufacturing Predictive Quality

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Beyond Model-Based Systems Engineering towards Managing Complexity 30

Beyond Model-Based Systems Engineering towards Managing Comp...

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30th CIRP Design on Design, CIRP Design 2020

作者： Masior, Jonathan Schneider, Benjamin Kürümlüoglu, Mehmet Riedel, Oliver Fraunhofer Institute for Industrial Engineering IAO Nobelstrasse 12 Stuttgart70569 Germany University of Stuttgart Institute for Human Factors and Technology Management IAT Nobelstrasse 12 Stuttgart70569 Germany University of Stuttgart Institute for Control Engineering of Machine Tools and Manufacturing Units ISW Seidenstrasse 36 Stuttgart70174 Germany

Systems Engineering domain lacks in methodologies for integration of development artefacts (e.g. requirements, functions, geometrics, or behavior) and enterprise models (e.g. organizational structures, processes, or IT infrastructure). Especially the rising complexity driven by integrated product service systems, smart services, systems of systems and cyber physical systems (CPS) leads to a new paradigm further on called Advanced Systems Engineering (ASE). It empowers enterprises to cope with such complex developments. The employment of ASE technologies in design and manufacturing enterprises requires a reachability of technical information and continuity along vertical and horizontal dimensions. A widely used approach to implement this is Model-based System Engineering (MBSE). This paper introduces a solution to achieve the goals of manufacturing excellence as well as complexity management by identifying and analyzing relevant requirements and possible approaches for an innovative simulation enabling and executable modelling language, the so-called advanced MBSE+. © 2017 The Authors. Published by Elsevier B.V.

关键词： Cyber Physical System

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AI-enhanced Identification, Inspection and Sorting for Reverse Logistics in Remanufacturing

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Procedia CIRP 2021年 98卷 300-305页

作者： Marian Schlüter Hannah Lickert Katharina Schweitzer Pinar Bilge Clemens Briese Franz Dietrich Jörg Krüger Fraunhofer IPK - Institute for Production Systems and Design Technology Pascalstr. 8-9 10587 Berlin Germany Technische Universität Berlin Institute for Machine Tools and Factory Management Pascalstr. 8-9 10587 Berlin Germany acatech – Deutsche Akademie der Technikwissenschaften Karolinenplatz 4 80333 München Germany

In a circular economy for remanufacturing, after each life cycle used products are returned to a remanufacturer for identification, inspection, sorting and reprocessing. Shortcomings and requirements of the remanufacturing market are identified through expert interviews and process analysis. A concept is proposed to enable an improved identification and a more objective inspection by enhancing the working environment and processes of sorting stations. Digitization and machine learning are applied on business data, using machine vision as well as sensor and actor skills of the worker. With an experimental case study on visual object recognition a positive impact on identification and thus sorting could be demonstrated.

关键词： Circular Economy Remanufacturing Reverse Logistics Sorting Computer Vision machine Learning Assistance System

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Influence on the microstructure of powder metallurgical metal foam by means of mechanical alloying 22nd

Influence on the microstructure of powder metallurgical meta...

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22nd Symposium on Composites, 2019

作者： Siebeck, Steve Trautmann, Maik Hohlfeld, Jörg Hipke, Thomas Wagner, Guntram Fraunhofer Institute for Machine Tools and Forming Technology IWU Reichenhainer Straße 88 Chemnitz09126 Germany Chemnitz University of Technology Professorship of Composites and Material Compounds Erfenschlager Straße 73 Chemnitz09125 Germany

ISBN: (纸本)9783035714531

Due to the process, metal foams produced by powder metallurgy have an inhomogeneous pore structure. The pore size variations from a few tenths of a millimeter up to the centimeter range is so typical. This leads to inconstant properties and makes the predictability of metal foam based components difficult. The present work deals with the approach to precondition the feedstock powder for the production of metal foams by mechanical alloying. Thereby, the homogeneous distribution of the blowing agent TiH2 is already present in the so-called composite powder. In addition, there is the possibility to add reinforcement particles with regard to a further improvement of the mechanical properties. Overall, this leads to a rather wide range of possible influencing factors and parameters to be varied. Accordingly, the present work is only a preview of a broad experimental program, taking into account the design of experiments and is currently being processed. In this first study, an alloy AA6060 has been used as the matrix material. The evaluated method provides a finer pore structure associated with a modified foaming behavior. © 2019 Trans Tech Publications Ltd, Switzerland.

关键词： Pore structure

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Hololens AR - Using Vuforia-Based Marker Tracking Together with Text Recognition in an Assembly Scenario

Hololens AR - Using Vuforia-Based Marker Tracking Together w...

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Sebastian Knopp Philipp Klimant Robert Schaffrath Eric Voigt Rayk Fritzsche Christoph Allmacher Institute for Machine Tools and Production Processes Chemnitz University of Technology Fraunhofer Institute for Machine Tools and Forming Technology IWU Chemnitz University of Technology Chemnitz Germany

ISBN: (纸本)9781728147666

Many real world applications for Microsoft HoloLens*-based applications suffer the problem of reliably recognizing and identifying movable objects within an environment. While the HoloLens is perfectly able to discern already known rooms, it still has troubles with reflecting surfaces or identically shaped objects. Using dedicated recognition libraries for each task poses the issue of shared resource access in the rather controlled HoloLens environment-In this poster we present a solution for scenario with hard to track objects and similarly shaped objects for an electrical cabinet assembly task, where the reflective cabinet is tagged with a marker and the prefabricated cables are differentiated by text-based labels.

关键词： HoloLens AR Vuforia Text recognition Application showcase Printed text recognition Argon Posters Cabinets Cabinet

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Considering energy-related factors in the simulation of logistics systems 19

Considering energy-related factors in the simulation of logi...

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Proceedings of the Winter Simulation Conference

作者： Moritz Poeting Bastian Prell Markus Rabe Tobias Uhlig Sigrid Wenzel TU Dortmund University Dortmund Germany Fraunhofer Institute for Machine Tools and Forming Technology IWU Chemnitz Germany Universität der Bundeswehr München Neubiberg Germany University of Kassel Kassel Germany

ISBN: (纸本)9781728132839

Traditionally, aspects such as emissions and energy consumption have to be taken into account for environmental and economic reasons when it comes to transport. In other areas of logistics, such as production logistics and intralogistics, the energy aspect is also becoming increasingly important. Existing literature has been recently reviewed in a contribution of the Arbeitsgemeinschaft Simulation (ASIM) to the Winter Simulation Conference 2018 (Uhlig et al. 2018) to develop a map of common approaches and best practices for manufacturing and logistics systems. In the paper presented here, as a complement we are focusing on the application of energy simulation in logistics to give a comprehensive overview and present exemplary case studies. Furthermore, we show a classification of approaches to combine energy aspects with simulation. Finally, we will discuss open questions and future trends in this field of research.

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Process behaviour of micro-textured CVD diamond thick film cutting tools during turning of Ti-6Al-4V

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Procedia CIRP 2020年 87卷 25-30页

作者： Eckart Uhlmann Danny Schröter Institute for Machine Tools and Factory Management (IWF) Technische Universität Berlin 10587 Berlin Germany Fraunhofer Institute for Production Systems and Design Technology (IPK) 10587 Berlin Germany

Currently, an efficient machining of high-performance materials, such as titanium alloy Ti-6Al-4V, represents a challenge in manufacturing. For the reduction of high cutting temperatures, micro-textured CVD diamond thick film cutting tools establish new potentials regarding the process productivity. The application of micro-textures allows a reduced contact area between chip and tool in order to decrease the generation of heat. Furthermore, a direct application of lubricant into the cutting zone is possible. The present study investigates the influence of micro-textured CVD diamond thick film cutting tools on the resulting process forces, chip formation as well as maximum cutting volumes during turning process. Part of the investigations was a variation of micro-texture trajectory with micro-textures parallel to the major cutting edge, perpendicular to the bisector of corner angle ε and curved along the cutting edge. The analyses showed that both positive and negative effects occur using micro-textures. The forces can be reduced by 14 % whereas the use of micro-textures leads to higher feed forces F f by 21 % as well as an additional notch effect in the micro-texture bottom.

关键词： CVD diamond thick film micro-texture Ti-6Al-4V turning

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Data augmentation for inertial sensor based human action recognition using deep learning

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AIP Conference Proceedings 2024年第1期2989卷

作者： Jan Kuschan Hristo Filaretov Jörg Krüger Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstr. 8-9 10587 Berlin Germany Industrial Automation Technology Institute of Machine Tools and Factory Management Technical University Berlin Pascalstr. 8-9 10587 Berlin Germany

Human Activity Recognition (HAR) approaches are predominantly based on supervised deep learning and benefit from large amounts of labeled data—an expensive resource. Data augmentation enriches labeled datasets by adding synthetic data, which is substantially cheaper, and often results in improved model performance, but is very rarely used for sensor data. This work explores data augmentation for inertial-sensor-based HAR by transforming the data through physically interpretable operations. The main studies were conducted on the Opportunity and the Overhead Car Assembly (OCA) datasets. For these experiments, only 20% of the available training data were used, and the experiments were conducted in an 8-fold cross-validation procedure over different subsets of the training *** results show that simple geometric augmentations can be beneficial in many cases. Timewarping proved to offer the most reliable single augmentation, improving the average F1 score of Opportunity from 0.570 to 0.597 and of OCA Mixed from 0.884 to 0.906. Combining augmentations improved the accuracy in almost all scenarios but to a degree comparable to timewarping. Applying augmentations on all the available training data improved the F1 score compared to the base case with no augmentations, although this effect is more pronounced for datasets with more similar training and test data: for the OCA Mixed variant, the average F1 score improved from 0.917 to 0.933, while for the OCA Leave-One-Out (LOT) variant, the average F1 score did not significantly change. For Opportunity, which similarly to OCA LOT uses a participant-based training-test split, the F1 score improved from 0.684 to 0.697.

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Preface

Lecture Notes in Production Engineering

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Lecture Notes in Production Engineering 2022年 Part F1160卷 v页

作者： Behrens, Bernd-Arno Brosius, Alexander Drossel, Welf-Guntram Hintze, Wolfgang Ihlenfeldt, Steffen Nyhuis, Peter Institute of Forming Technology and Machines Garbsen Germany Institute of Manufacturing Science and Engineering Dresden Germany Institute for Machine Tools and Forming Technology Chemnitz Germany Institute of Production Management and Technology Hamburg Germany Institute of Mechatronic Engineering Dresden Germany Institute of Production Systems and Logistics Garbsen Germany

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