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Application of biological transformation to foster positive urban production

Procedia CIRP 2020年 90卷 2-9页

作者： Christoph Herrmann Lennart Büth Max Juraschek Tim Abraham Lothar Schäfer Chair of Sustainable Manufacturing & Life Cycle Engineering Institute of Machine Tools and Production Technology Technische Universität Braunschweig Germany Fraunhofer Institute for Surface Engineering and Thin Films Braunschweig Germany

Urbanization and the limited environmental carrying capacity of our planet are crucial challenges for human kind. Industrial value creation and the production of goods have a significant role in the worldwide emissions pattern. A biologically oriented conception of technical systems with the goal of a sustainable added value is summarized as “biological transformation”. By bringing together the concentration of consumption, waste generation and more, in urban areas, with the biologicalisation of industrial value creation an opportunity arises to actively create bio-based positive urban production. Based on the principles of the biological transformation a framework is derived, stretching the design space between spatial urban levels and production life cycle stages, thereby fostering positive bio-based urban production. The design space is then applied, allocating current realization and envisioned cases as well as giving recommendations for future research topics.

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Production rescheduling through product quality prediction

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Procedia Manufacturing 2021年 54卷 142-147页

作者： Maik Frye Dávid Gyulai Júlia Bergmann Robert H. Schmitt Fraunhofer Institute for Production Technology (IPT) Steinbachstraße 17 52074 Aachen Germany EPIC Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) 13-17 Kende Budapest 1111 Hungary Laboratory for Machine Tools WZL RWTH Aachen University Steinbachstraße 19. 52074 Aachen Germany

In production management, efficient scheduling is key towards smooth and balanced production. Scheduling can be well-supported by real-time data acquisition systems, resulting in decisions that rely on actual or predicted status of production environment and jobs in progress. Utilizing advanced monitoring systems, prediction-based rescheduling method is proposed that can react on in-process scrap predictions, performed by machine learning algorithms. Based on predictions, overall production can be rescheduled with higher efficiency, compared to rescheduling after completion of the whole machining process with realization of scrap. Series of numerical experiments are presented to demonstrate potentials in prediction-based rescheduling, with early-stage scrap detection.

关键词： machine Learning Production Scheduling Product Quality Prediction Data Quality

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Influence of Part Geometry and Feature Size on the Resulting Microstructure and Mechanical Properties of the Case Hardening Steel 16MnCr5 processed by Laser Powder Bed Fusion

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Procedia CIRP 2021年 104卷 726-731页

作者： Matthias Schmitt Florian Gerstl Max Boesele Max Horn Georg Schlick Johannes Schilp Gunther Reinhart Fraunhofer-Institute for Casting Composite and Processing Technology IGCV Am Technologiezentrum 10 D-86159 Augsburg Germany University of Augsburg Chair of Digital Manufacturing Faculty of Applied Computer Sience Eichleitnerstr. 30 D-86159 Augsburg Germany Institute for Machine Tools and Industrial Management (iwb) Technical University of Munich D-85748 Garching Germany

Additive manufacturing, especially laser powder bed fusion (LPBF), allows the build-up of complex parts. However, the resulting microstructure is not only influenced by the manufacturing parameters but also by the part geometry due to the influence on heat dissipation. At first, six geometric features are identified which can affect heat dissipation. Geometric samples of a case-hardening steel are produced via LPBF. Analysis of the microstructure and the mechanical properties show that the cross-sectional area and overhang angles have significant influence resulting in a hardness differences of more than 90 HV while increasing the ferrite content with increasing overhang angles. For further investigation, the results are analytically linked to the part density.

关键词： case-hardening steel 16MnCr5 influence of geometry microstructure heat dissipation

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Environmental and technical evaluation of additive manufacturing: Enabling process chain perspective by energy value stream mapping

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Procedia CIRP 2022年 105卷 440-445页

作者： Mathias Wiese Christopher Rogall Nadja Henningsen Christoph Herrmann Sebastian Thiede Polymer Additive Manufacturing Center AUDI AG Auto-Union-Straße 1 Ingolstadt 85045 Germany Chair of Sustainable Manufacturing and Life Cycle Engineering Institute of Machine Tools and Production Technology Technische Universität Braunschweig Langer Kamp 19b Braunschweig 38106 Germany Fraunhofer Institute for Surface Engineering and Thin Films (IST) Bienroder Weg 54 E Braunschweig 38108 Germany Manufacturing Systems Faculty of Engineering Technology Department of Design Production & Management University of Twente De Horst 2 Building 20 Enschede 7522 LW The Netherlands

Additive manufacturing (AM) currently evolves from a prototyping process to an alternative manufacturing process for end-use parts, advancing into fabrication of low to medium product volumes. Moreover, increasing awareness of environmental impacts of manufacturing lead to the necessity of holistic evaluation among traditional evaluation criteria in the technical and economic domains. However, existing evaluation approaches seldom consider a complete AM process chain and are lacking a production-oriented notation which enables comparability across technologies and support in identification of improvement potentials on technical and environmental level. To address this gap, this paper highlights the way from structured data acquisition to setup of an energy value stream map (EVSM) for AM process chains in end-use part production, augmenting methods of lean manufacturing by the energy dimension. Consequently, it contributes to a holistic and transparent process chain perspective to assess AM as a manufacturing alternative. The proposed methodology is applied to a case study covering two different process chains, a first based on powder bed fusion via Multi-jet Fusion (MJF) and a second utilizing vat-photopolymerization via Continuous Liquid Interphase Printing (CLIP). While the MJF process chain’s energetical hot spot is situated in the printing process itself, the hot spot for CLIP is found in thermal post-processing, exceeding the comparably efficient printing process by magnitudes and ultimately resulting in higher energy intensity per part compared to the MJF process chain. These results highlight the necessity of a holistic evaluation method for complete AM-based process chains and their influence on the product properties. Insights may help engineers, designers and decision makers in pre-selection of suitable manufacturing strategies with a more complete view on AM process chains.

关键词： Additive manufacturing rapid manufacturing process chains value stream mapping sustainable manufacturing decision support

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Implementation of the machine Tool-Specific Current and Voltage Control Characteristics in Multiphysics Simulation of Electrochemical Precision Machining

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Procedia CIRP 2019年 82卷 237-242页

作者： Ingo Schaarschmidt Matthias Hackert-Oschätzchen Gunnar Meichsner Mike Zinecker Andreas Schubert Professorship Micromanufacturing Technology Chemnitz University of Technology 09107 Chemnitz Germany Fraunhofer Institute for Machine Tools and Forming Technology IWU 09126 Chemnitz Germany

In this study a multiphysics simulation model of the pulsed electrochemical machining (PECM) with oscillating cathode is shown. The focus of this study is on the implementation of machine tool-specific control characteristics in combination with physical phenomena and material removal. As a result, the implementation of the machine tool control characteristics leads to an increase of simulation accuracy of the multiphysics model. The simulation results are compared with results of previous simulations and experimental investigations.

关键词： Electrochemical machining (ECM) Material removal High strength steel

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Structural health monitoring for fibre reinforced composites produced by the winding process 22

Structural health monitoring for fibre reinforced composites...

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22nd International Conference on Composite Materials, ICCM 2019

作者： Naumann, Mario D. Körner, Enrico Heinrich, Michael Kroll, Lothar Department of Lightweight Structures and Polymer Technology Technical University of Chemnitz Chemnitz Germany Fraunhofer Institute for Machine Tools and Forming Technology Volkswagen AG Federal Cluster of Excellence MERGE Technical University of Chemnitz Chemnitz Germany Fraunhofer Project Center for Advanced Lightweight Technologies Opole Poland

In recent years, the power density as a ratio of internal pressure to mass of pressure vessels has steadily increased, which is particularly advantageous for the transportation of gases as fuel in vehicles. At the same time, high safety requirements combined with a high degree of lightweight construction usually correlate with an increased demand for cost-intensive high-performance materials. Also with the increasing introduction of hydrogen-powered vehicles, high safety measures play a central role for pressure vessels, which are usually serially manufactured. In order to provide progressive series solutions for this, new safety systems are moving into the focus of scientific efforts. Within the scope of intensive research work, the complete integration of a metallic sensor wire for the deformation measurement of a fibre-plastic composite pressure vessel as well as its functional verification is therefore being investigated. The structural integration of the sensor wire is based on a direct method, the so-called winding process. In the winding process, the reinforcing fibres are applied to a positive core. This results in closed moulded bodies in fiber reinforced plastic (FRP) design such as pipes, tanks, axles, pressure vessels and shafts. With the aid of a winding machine, a core (mandrel), which forms the shape of the component, is set in rotation and the reinforcing fibre is pulled off a creel with a defined yarn tension. At the same time, the sensor wire is fed through the same thread eye. The sensor wire thus follows the reinforcing fibre. This results in an exact positioning of the sensor wire along the reinforcing fibers and allows the measurement according to the stress of the composite structure. For the impregnation of the reinforcement fibres with the resin system, these are passed through a resin bath and then wound onto the mandrel. Furthermore, the impregnation of the sensor wires is carried out indirectly via contact of the sensor wires with the imp

关键词： Fiber reinforced plastics

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Using anomaly detection and classification to support failure recovery of plants

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AIP Conference Proceedings 2024年第1期2989卷

作者： Niklas Grambow Lisa-Marie Fenner Oliver Heimann Jörg Krüger Department for Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology Pascalstr. 8-9 10587 Berlin Germany Institute for Machine Tools and Factory Management Technische Universität Berlin Pascalstr. 8-9 10587 Berlin Germany

Failure recovery of machines is a great challenge for the plant operator. Usually, a stopped plant is quite costly and has to be put back into operation fast. In that case, experienced personnel are essential. Only their knowledge and broad understanding of the interaction of the various components allows a reliable identification of the underlying problem and enables a successful recovery. Digitization can help to address this problem. Knowledge can be stored and shared digitally across plants, locations and companies. In addition, an ever-increasing amount of machine and process data is digitally available which can further support the problem-solving process. We present a new approach to machine maintenance that relies on machine learning to help identify the underlying technical problem that led to the machines error. In contrast to predictive maintenance, our system is employed reactively and focusses on the identification and classification of singular failures rather than a predictive monitoring of the plants state. To support the operator in fault recovery, we use anomaly detection to identify the deviations in the data of the processes prior to the fault. This allows the system to generate hints as to where to look for the underlying cause of the error and helps the technician make an educated guess. Additionally, an error classification algorithm also allows troubleshooting information to be linked directly to the data. Thereby, when an error reoccurs, the knowledge gained in the first occurrence is immediately made available to the operator through information linked to the error *** this article, we present the concept behind our anomaly detection system and the required software architecture to combine the different data sources. Furthermore, we discuss the results from an experiment that simulates an optical part inspection using a camera based system, where we introduce different errors into the process and are able to demonstrate the capability

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Revisiting machinability assessment: Towards total machining performance

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CIRP Annals 2025年

作者： Jawahir, I.S. Attia, Helmi Dix, Martin Ghadbeigi, Hassan Liao, Zhirong Schoop, Julius Shokrani, Alborz University of Kentucky LexingtonKY United States Mechanical Engineering Department McGill University/Aerospace Manufacturing Technology Centre National Research Council Canada MontrealQC Canada Fraunhofer Institute for Machine Tools and Forming Technology IWU University of Technology Chemnitz Chemnitz Germany School of Mechanical Aerospace and Civil Engineering University of Sheffield Sheffield United Kingdom Faculty of Engineering University of Nottingham Nottingham United Kingdom Department of Mechanical Engineering University of Bath Bath United Kingdom

The term "machinability", introduced over hundred years ago, is vague and cannot fully describe the performance of machining systems. Machinability databases established over many decades are outdated: missing recent advances, e.g., cutting tool grades, geometry, coatings, and cutting fluids effects. This keynote paper summarizes findings of a CIRP-sponsored three-year collaborative study in five interrelated topics. The paper presents a critical review of the state-of-the-art on these topics, the results of two major round robin tests, three industry-based case studies, and a novel predictive system of machining performance, utilizing advanced deep learning methods. Outlook and future directions are also presented. © 2025 CIRP

关键词： Drills

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Empirical Modeling of Abrasive Waterjet Process for Controlled Depth Machining of Dense Segmented Ceramic Thermal Barrier Coatings

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Procedia CIRP 2021年 102卷 355-360页

作者： J.P. Borrmann M. Dadgar J.-E. Döring T. Herrig T. Bergs Siemens Energy Global GmbH & Co. KG Campus Boulevard 79 52074 Aachen Germany Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany

Abrasive waterjet (AWJ) controlled depth machining shows promise to be one of the most efficient non-conventional structuring techniques for dense segmented Thermal Barrier Coatings (STBC) on turbomachinery hot gas components made of Yttria-Stabilized Zirconia. Exemplary applications in the field of gas turbine technology are engraving of structures to optimize gas turbine performance and the stripping process of TBC within the repair process chain. As there are no comprehensive process models available, the development of an appropriate AWJ machining process is demanding. Thus, deeper process understanding and modeling need to be investigated. This paper shows an empirical modeling of AWJ process for controlled depth machining of dense segmented TBC material. The practical trials are based on a Design of Experiments (DoE). The investigated influencing parameters are water pressure, abrasive mass flow, feed rate, hatch distance and machining angle. The considered target variables are ablation depth and surface roughness. Furthermore, the process stability is investigated. The developed empirical model results in an acceleration of process parameter determinations.

关键词： Waterjet Machining Ceramics Modeling Design of Experiments (DoE) Segmented Thermal Barrier Coating

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Requirements for Economic Analysis of 5G technology Implementation in Smart Factories from End-User Perspective

Requirements for Economic Analysis of 5G Technology Implemen...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Raphael Kiesel Robert H. Schmitt Fraunhofer Institute for Production Technology IPT Aachen Germany Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Aachen

ISBN: (数字)9781728144900

ISBN: (纸本)9781728144917

The estimated economic impact of 5G technology in production industry is immense. Until 2030, worldwide production industry gross domestic product is expected to by up to $740 billion [4]. Providing low latencies, high data transmission rates and the possibility of operating many devices simultaneously in narrowly restricted radio cells, 5G is expected to meet the demands of networked production systems and has great potential to accelerate the ongoing digital transformation. Despite these prospected advantages, benefits of integrating 5G technology in a production process can barely be quantified yet. 40 % of enterprises cite poor measurability of economic benefits of 5G for their specific processes as key concern [6]. Thus, improvement potential for production processes and their monetary benefit need to be quantified in order to provide decision-makers with a sound base for their investment decisions. This paper describes the requirements and a first approach of a model to quantify economic potential of 5G technology in production. Therefore, existing approaches and models to quantify economic benefits of 5G technology and of digitalization in production in general are analyzed. Then, the model is derived. In the end, future research needs are given.

关键词： Production 5G mobile communication Economics Industries Analytical models Smart manufacturing Investment

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