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  • Mit dem Übergang zur Logistik 4.0 hat der zunehmende Bedarf an autonomen mobilen Robotern (AMR) in der Logistik die Komplexität der Flottensteuerung in dynamischen Umgebungen erhöht. Reinforcement Learning (RL), insbesondere dezentrale RLAlgorithmen, haben sich aufgrund ihrer Fähigkeit, in unsicheren Umgebungen zu lernen, als potenzielle Lösung erwiesen. Während sich diskrete RL-Strukturen bewährt haben, bleibt ihre Anpassungsfähigkeit in der Logistik aufgrund ihrer inhärenten Einschränkungen fraglich. In diesem Beitrag wird eine vergleichende Analyse kontinuierlicher RL-Algorithmen - Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG) und Prox-imal Policy Optimization (PPO) - im Kontext der Steuerung eines Turtlebot3 in einem Lagerszenario vorgestellt. Unsere Ergebnisse zeigen A2C als Spitzenreiter in Bezug auf Erfolgsrate und Trainingszeit, während DDPG bei der Minimierung der Episodenlänge punktet und PPO lediglich mit einer geringen Trainingsdauer aufwarten kann. Diese Studie unterstreicht das Potenzial von kontinuierlichen RL-Algorithmen, insbesondere A2C, für die Zukunft des AMR-Flottenmanagements in der Logistik, wobei gerade im Bereich des Finetunings der Algorithmen noch viel Arbeit zu tun ist. With the transition to Logistics 4.0, the increasing demand for autonomous mobile robots (AMR) in logistics has amplified the complexity of fleet control in dynamic environments. Reinforcement learning (RL), particularly decentralized RL algorithms, has emerged as a potential solution given its ability to learn in uncertain terrains. While discrete RL structures have shown merit, their adaptability in logistics remains questionable due to their inherent limitations. This paper presents a comparative analysis of continuous RL algorithms - Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) - in the context of controlling a Turtlebot3 within a warehouse scenario. Our findings reveal A2C as the frontrunner in terms of success rate and training time, while DDPG excels step minimization while PPO distinguishes itself primarily through its relatively short training duration. This study underscores the potential of continuous RL algorithms, especially A2C, in the future of AMR fleet management in logistics. Significant work remains to be done, particularly in the area of algorithmic fine-tuning.

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  • In diesem Beitrag wird ein neuartiger Ansatz zum Benchmarking von Indoor-Lokalisierungssystemen (ILS) für mobile Roboter in Lager- und Produktion-sumgebungen vorgestellt. Die Studie konzentriert sich auf verschiedene Lokalisierungstechnologien, die üblicherweise in der mobilen Robotik verwendet werden, und implementiert transparente und vergleichbare Leistungsmetriken, ein automatisiertes Experimentierverfahren und einen intuitiven Ansatz zur Leistungsvisualisierung. Die Experimente wurden mit einem speziell angefertigten Roboter durchgeführt, der mit verschiedenen Sensoren ausgestattet war, darunter LiDAR-, UWB- und Vision-Systeme. Es wird eine Methode vorgeschlagen, um die Auswirkungen von Umgebungsfaktoren wie Beleuchtung, Reflektivität und Hindernisse auf die Lokalisierungsleistung systematisch zu analysieren. Die Ergebnisse geben Aufschluss über die Robustheit und Genauigkeit des Systems unter verschiedenen Bedingungen. Die Studie ermöglicht eine effizientere experimentelle Analyse von Sensorfusions- und Optimierungsstrategien, um eine optimale Leistung zu erzielen, und bietet einen Arbeitsablauf für die effiziente Untersuchung von Sensorfusionskonzepten anhand realer Daten. This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.

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    Authors: Krüger, Stephan; Grafelmann, Michaela; Jahn, Carlos;

    Small and medium-sized (SME) logistics hubs are characterized by a variety of customer relationships, different services offered, and diverse organizational interfaces. Increased requirements for workflows that run smoothly, at best digitally, are often met by using individual IT systems at hubs like inland intermodal terminals. In this context, the development and introduction of systems are rarely characterized by a uniform strategy, but by short-term requirements and interim solutions. This paper aims to develop an IT reference model for SME inland terminals. The focus is on supporting the independent and structured further development of processes and IT landscapes by the terminals. The paper is based on a project, which was carried out in exchange with experts and involved parties as well as based on a literature analysis to highlight SME- and branch-specific issues. Modeling the current situation creates a basis for identifying weaknesses and target landscapes. Reference process models assist with the systematic mapping and analysis of IT and process landscapes and hold opportunities to identify potentials to increase productivity, reduce costs and avoid redundancies. It consists of many process models, tools, and recommendations for action, which together comprise a “help for self-help” approach. Implications for making process models more flexible to respond to external demands were considered.

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    https://doi.org/10.3233/atde23...
    Part of book or chapter of book . 2023 . Peer-reviewed
    License: CC BY NC
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    Other ORP type . Conference object . 2023
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    Conference object . 2023
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      https://doi.org/10.3233/atde23...
      Part of book or chapter of book . 2023 . Peer-reviewed
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      Other ORP type . Conference object . 2023
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    Authors: Philip Koch; Parth Rawal; Nico Töpfer; Tim Haß; +2 Authors

    AbstractThere is a continuing trend in the aircraft industry to automate production. In order to be able to react to shortages of skilled workers, high order fluctuations and machine breakdowns, cost-effective, mobile and flexible systems are required to support the workers. This paper focuses on the integration of existing skill-based engineering concepts into production using standard OPC Unified Architecture interface, where production systems can be built quickly by simply interconnecting modules. The interconnected modules together form higher level subsystems enabling reusability of the individual modules as well as the assembled subsystems across several use cases. The approach is evaluated on a production related mobile robot system, whose task is to drive to the workstation, reference the component and drill holes in a vertical tail plane section of an aircraft. All devices from different suppliers contain skill-based modules based on standards defined by OPC Foundation and communicate via OPC UA-based Client/Server communication.

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    TUHH Open Research (TORE)
    Article . Other ORP type . 2023
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    TUHH Open Research
    Article . 2023
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    Production Engineering
    Article . 2023 . Peer-reviewed
    License: CC BY
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    Article . 2023
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      Article . Other ORP type . 2023
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      Production Engineering
      Article . 2023 . Peer-reviewed
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    Authors: Jan-Erik Rath; Robert Graupner; Thorsten Schüppstuhl;

    The demand for lightweight materials, such as fiber-reinforced plastics (FRP), is constantly growing. However, current FRP production mostly relies on expensive molds representing the final part geometry, which is not economical for prototyping or highly individualized products, such as in the medical or sporting goods sector. Therefore, inspired by incremental sheet metal forming, we conduct a systematic functional analysis on new processing methods for shaping woven FRP without the use of molds. Considering different material combinations, such as dry fabric with thermoset resin, thermoset prepreg, thermoplastic commingled yarn weave and organo sheets, we propose potential technical implementations of novel dieless forming techniques, making use of simple robot-guided standard tools, such as hemispherical tool tips or rollers. Feasibility of selected approaches is investigated in basic practical experiments with handheld tools. Results show that the main challenge of dieless local forming, the conservation of already formed shapes while allowing drapability of remaining areas, is best fulfilled by local impregnation, consolidation and solidification of commingled yarn fabric, as well as concurrent forming of prepreg and metal wire mesh support material. Further research is proposed to improve part quality. Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

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    Machines
    Other literature type . Article . 2023 . Peer-reviewed
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    Machines
    Article . 2023
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      Machines
      Other literature type . Article . 2023 . Peer-reviewed
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      Machines
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    Authors: Daubitz, Stephan; Aberle, Christoph; Schwedes, Oliver; Gertz, Carsten;

    Final report of the MobileInclusion project, which was funded by German Research Foundation (DFG) and focused on transport disadvantage in Berlin and Hamburg. Project duration: 2018-2021 Endbericht des DFG-Projekts MobileInclusion, das sich mit Mobilität und sozialer Exklusion in Berlin und Hamburg befasste. Laufzeit: 2018-2021

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    https://doi.org/10.52038/97836...
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    Book . 2023
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      https://doi.org/10.52038/97836...
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      Book . 2023
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    Authors: Maximilian Neidhardt; Robin Mieling; Marcel Bengs; Alexander Schlaefer;

    AbstractRobotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a challenge. Image-based force estimation offers a promising solution without the need to integrate sensors into surgical tools. In this indirect approach, interaction forces are derived from the observed deformation, with learning-based methods improving accuracy and real-time capability. However, the relationship between deformation and force is determined by the stiffness of the tissue. Consequently, both deformation and local tissue properties must be observed for an approach applicable to heterogeneous tissue. In this work, we use optical coherence tomography, which can combine the detection of tissue deformation with shear wave elastography in a single modality. We present a multi-input deep learning network for processing of local elasticity estimates and volumetric image data. Our results demonstrate that accounting for elastic properties is critical for accurate image-based force estimation across different tissue types and properties. Joint processing of local elasticity information yields the best performance throughout our phantom study. Furthermore, we test our approach on soft tissue samples that were not present during training and show that generalization to other tissue properties is possible.

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    Scientific Reports
    Article . 2023 . Peer-reviewed
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    Scientific Reports
    Article . 2023
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      Scientific Reports
      Article . 2023 . Peer-reviewed
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      Scientific Reports
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    Authors: J. Koch; G. Lotzing; H. Eschen; K. Moenck; +1 Authors
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    Procedia CIRP; TUHH Open Research (TORE)
    Article . Other ORP type . 2023 . Peer-reviewed
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  • Reducing the environmental impact of porcelain tile production while maintaining cost-effectiveness is challenging. This work introduced a novel modeling approach for optimizing a standard composition range comprising kaolinite (15–38 wt.%), illite (0–20 wt.%), quartz (20–40 wt.%), and feldspar (20–45 wt.%) to establish a robust composition interval for porcelain stoneware tiles. The proposed study considers several factors, such as composition impact on the manufacturing sequence, production costs, and CO2 emission. A flowsheet simulation database was generated by coupling the Dyssol framework with MATLAB. This study investigated the influence of raw material composition within the process sequence, the total CO2 emissions, and production costs within the contexts of Spain and Brazil, two of the top five global producers. Granules with a higher proportion of talc and illite exhibit reduced moisture content after spray drying, and these combinations have lower green body porosity after compaction. The addition of talc allowed for decreased porosity content after compaction reduced firing temperature, and lowered costs and CO2 emissions despite the higher prices associated with talc. The proposed simulation methodology offers a powerful decision-making tool for optimizing raw material composition to minimize cost and CO2 emissions in the porcelain tile production. This methodology represents an early stride toward integrating digital twin methodologies within the ceramic tile sector, facilitating improved process regulation, and promoting the adoption of digital technologies.

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  • Green Hydrogen is a promising energy carrier for the future industry. To meet the accompanying increase in demand for electrolyzers, an equivalent increase in production capacity is essential. This can be achieved through efficient assembly orchestrations based on continuous assembly progress detection. This paper contributes to the necessary data acquisition during the assembly process. Various methods from the state of the art are presented, evaluated and compared against each other. Finally, a modular concept for data acquisition on the construction site of electrolysis plants based on data fusion is proposed.

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  • Mit dem Übergang zur Logistik 4.0 hat der zunehmende Bedarf an autonomen mobilen Robotern (AMR) in der Logistik die Komplexität der Flottensteuerung in dynamischen Umgebungen erhöht. Reinforcement Learning (RL), insbesondere dezentrale RLAlgorithmen, haben sich aufgrund ihrer Fähigkeit, in unsicheren Umgebungen zu lernen, als potenzielle Lösung erwiesen. Während sich diskrete RL-Strukturen bewährt haben, bleibt ihre Anpassungsfähigkeit in der Logistik aufgrund ihrer inhärenten Einschränkungen fraglich. In diesem Beitrag wird eine vergleichende Analyse kontinuierlicher RL-Algorithmen - Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG) und Prox-imal Policy Optimization (PPO) - im Kontext der Steuerung eines Turtlebot3 in einem Lagerszenario vorgestellt. Unsere Ergebnisse zeigen A2C als Spitzenreiter in Bezug auf Erfolgsrate und Trainingszeit, während DDPG bei der Minimierung der Episodenlänge punktet und PPO lediglich mit einer geringen Trainingsdauer aufwarten kann. Diese Studie unterstreicht das Potenzial von kontinuierlichen RL-Algorithmen, insbesondere A2C, für die Zukunft des AMR-Flottenmanagements in der Logistik, wobei gerade im Bereich des Finetunings der Algorithmen noch viel Arbeit zu tun ist. With the transition to Logistics 4.0, the increasing demand for autonomous mobile robots (AMR) in logistics has amplified the complexity of fleet control in dynamic environments. Reinforcement learning (RL), particularly decentralized RL algorithms, has emerged as a potential solution given its ability to learn in uncertain terrains. While discrete RL structures have shown merit, their adaptability in logistics remains questionable due to their inherent limitations. This paper presents a comparative analysis of continuous RL algorithms - Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) - in the context of controlling a Turtlebot3 within a warehouse scenario. Our findings reveal A2C as the frontrunner in terms of success rate and training time, while DDPG excels step minimization while PPO distinguishes itself primarily through its relatively short training duration. This study underscores the potential of continuous RL algorithms, especially A2C, in the future of AMR fleet management in logistics. Significant work remains to be done, particularly in the area of algorithmic fine-tuning.

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  • In diesem Beitrag wird ein neuartiger Ansatz zum Benchmarking von Indoor-Lokalisierungssystemen (ILS) für mobile Roboter in Lager- und Produktion-sumgebungen vorgestellt. Die Studie konzentriert sich auf verschiedene Lokalisierungstechnologien, die üblicherweise in der mobilen Robotik verwendet werden, und implementiert transparente und vergleichbare Leistungsmetriken, ein automatisiertes Experimentierverfahren und einen intuitiven Ansatz zur Leistungsvisualisierung. Die Experimente wurden mit einem speziell angefertigten Roboter durchgeführt, der mit verschiedenen Sensoren ausgestattet war, darunter LiDAR-, UWB- und Vision-Systeme. Es wird eine Methode vorgeschlagen, um die Auswirkungen von Umgebungsfaktoren wie Beleuchtung, Reflektivität und Hindernisse auf die Lokalisierungsleistung systematisch zu analysieren. Die Ergebnisse geben Aufschluss über die Robustheit und Genauigkeit des Systems unter verschiedenen Bedingungen. Die Studie ermöglicht eine effizientere experimentelle Analyse von Sensorfusions- und Optimierungsstrategien, um eine optimale Leistung zu erzielen, und bietet einen Arbeitsablauf für die effiziente Untersuchung von Sensorfusionskonzepten anhand realer Daten. This paper introduces a novel approach to benchmarking Indoor Localization Systems (ILS) for mobile robots in warehouse and manufacturing contexts. The study focuses on diverse localization technologies commonly used in mobile robotics and implements transparent and comparable performance metrics, an automated experimental procedure, as well as an intuitive performance visualization approach. Experiments were conducted using a custom-built robot equipped with various sensors, including LiDAR, Ultra-Wideband (UWB), and vision systems. A process for systematically analyzing the impact of environmental factors such as lighting, reflectivity, and obstacles on localization performance is proposed. The results provide insights into system robustness and accuracy under different conditions. The study enables more efficient experimental analysis of sensor fusion and optimization strategies for achieving optimal performance and offers a workflow to efficiently investigate sensor fusion concepts using real data.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Krüger, Stephan; Grafelmann, Michaela; Jahn, Carlos;

    Small and medium-sized (SME) logistics hubs are characterized by a variety of customer relationships, different services offered, and diverse organizational interfaces. Increased requirements for workflows that run smoothly, at best digitally, are often met by using individual IT systems at hubs like inland intermodal terminals. In this context, the development and introduction of systems are rarely characterized by a uniform strategy, but by short-term requirements and interim solutions. This paper aims to develop an IT reference model for SME inland terminals. The focus is on supporting the independent and structured further development of processes and IT landscapes by the terminals. The paper is based on a project, which was carried out in exchange with experts and involved parties as well as based on a literature analysis to highlight SME- and branch-specific issues. Modeling the current situation creates a basis for identifying weaknesses and target landscapes. Reference process models assist with the systematic mapping and analysis of IT and process landscapes and hold opportunities to identify potentials to increase productivity, reduce costs and avoid redundancies. It consists of many process models, tools, and recommendations for action, which together comprise a “help for self-help” approach. Implications for making process models more flexible to respond to external demands were considered.

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    https://doi.org/10.3233/atde23...
    Part of book or chapter of book . 2023 . Peer-reviewed
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    Other ORP type . Conference object . 2023
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      https://doi.org/10.3233/atde23...
      Part of book or chapter of book . 2023 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Philip Koch; Parth Rawal; Nico Töpfer; Tim Haß; +2 Authors

    AbstractThere is a continuing trend in the aircraft industry to automate production. In order to be able to react to shortages of skilled workers, high order fluctuations and machine breakdowns, cost-effective, mobile and flexible systems are required to support the workers. This paper focuses on the integration of existing skill-based engineering concepts into production using standard OPC Unified Architecture interface, where production systems can be built quickly by simply interconnecting modules. The interconnected modules together form higher level subsystems enabling reusability of the individual modules as well as the assembled subsystems across several use cases. The approach is evaluated on a production related mobile robot system, whose task is to drive to the workstation, reference the component and drill holes in a vertical tail plane section of an aircraft. All devices from different suppliers contain skill-based modules based on standards defined by OPC Foundation and communicate via OPC UA-based Client/Server communication.

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    TUHH Open Research (TORE)
    Article . Other ORP type . 2023
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    TUHH Open Research
    Article . 2023
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    Production Engineering
    Article . 2023 . Peer-reviewed
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    Fraunhofer-ePrints
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      Production Engineering
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    Authors: Jan-Erik Rath; Robert Graupner; Thorsten Schüppstuhl;

    The demand for lightweight materials, such as fiber-reinforced plastics (FRP), is constantly growing. However, current FRP production mostly relies on expensive molds representing the final part geometry, which is not economical for prototyping or highly individualized products, such as in the medical or sporting goods sector. Therefore, inspired by incremental sheet metal forming, we conduct a systematic functional analysis on new processing methods for shaping woven FRP without the use of molds. Considering different material combinations, such as dry fabric with thermoset resin, thermoset prepreg, thermoplastic commingled yarn weave and organo sheets, we propose potential technical implementations of novel dieless forming techniques, making use of simple robot-guided standard tools, such as hemispherical tool tips or rollers. Feasibility of selected approaches is investigated in basic practical experiments with handheld tools. Results show that the main challenge of dieless local forming, the conservation of already formed shapes while allowing drapability of remaining areas, is best fulfilled by local impregnation, consolidation and solidification of commingled yarn fabric, as well as concurrent forming of prepreg and metal wire mesh support material. Further research is proposed to improve part quality. Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

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    Machines
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    Machines
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      Machines
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      Machines
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    Authors: Daubitz, Stephan; Aberle, Christoph; Schwedes, Oliver; Gertz, Carsten;

    Final report of the MobileInclusion project, which was funded by German Research Foundation (DFG) and focused on transport disadvantage in Berlin and Hamburg. Project duration: 2018-2021 Endbericht des DFG-Projekts MobileInclusion, das sich mit Mobilität und sozialer Exklusion in Berlin und Hamburg befasste. Laufzeit: 2018-2021

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    https://doi.org/10.52038/97836...
    Book . 2023 . Peer-reviewed
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    Book . 2023
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      https://doi.org/10.52038/97836...
      Book . 2023 . Peer-reviewed
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      Book . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Maximilian Neidhardt; Robin Mieling; Marcel Bengs; Alexander Schlaefer;

    AbstractRobotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a challenge. Image-based force estimation offers a promising solution without the need to integrate sensors into surgical tools. In this indirect approach, interaction forces are derived from the observed deformation, with learning-based methods improving accuracy and real-time capability. However, the relationship between deformation and force is determined by the stiffness of the tissue. Consequently, both deformation and local tissue properties must be observed for an approach applicable to heterogeneous tissue. In this work, we use optical coherence tomography, which can combine the detection of tissue deformation with shear wave elastography in a single modality. We present a multi-input deep learning network for processing of local elasticity estimates and volumetric image data. Our results demonstrate that accounting for elastic properties is critical for accurate image-based force estimation across different tissue types and properties. Joint processing of local elasticity information yields the best performance throughout our phantom study. Furthermore, we test our approach on soft tissue samples that were not present during training and show that generalization to other tissue properties is possible.

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    Scientific Reports
    Article . 2023 . Peer-reviewed
    License: CC BY
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    Scientific Reports
    Article . 2023
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      Scientific Reports
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      Scientific Reports
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      Data sources: DOAJ
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    Authors: J. Koch; G. Lotzing; H. Eschen; K. Moenck; +1 Authors
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    Procedia CIRP; TUHH Open Research (TORE)
    Article . Other ORP type . 2023 . Peer-reviewed
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      Article . Other ORP type . 2023 . Peer-reviewed
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  • Reducing the environmental impact of porcelain tile production while maintaining cost-effectiveness is challenging. This work introduced a novel modeling approach for optimizing a standard composition range comprising kaolinite (15–38 wt.%), illite (0–20 wt.%), quartz (20–40 wt.%), and feldspar (20–45 wt.%) to establish a robust composition interval for porcelain stoneware tiles. The proposed study considers several factors, such as composition impact on the manufacturing sequence, production costs, and CO2 emission. A flowsheet simulation database was generated by coupling the Dyssol framework with MATLAB. This study investigated the influence of raw material composition within the process sequence, the total CO2 emissions, and production costs within the contexts of Spain and Brazil, two of the top five global producers. Granules with a higher proportion of talc and illite exhibit reduced moisture content after spray drying, and these combinations have lower green body porosity after compaction. The addition of talc allowed for decreased porosity content after compaction reduced firing temperature, and lowered costs and CO2 emissions despite the higher prices associated with talc. The proposed simulation methodology offers a powerful decision-making tool for optimizing raw material composition to minimize cost and CO2 emissions in the porcelain tile production. This methodology represents an early stride toward integrating digital twin methodologies within the ceramic tile sector, facilitating improved process regulation, and promoting the adoption of digital technologies.

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  • Green Hydrogen is a promising energy carrier for the future industry. To meet the accompanying increase in demand for electrolyzers, an equivalent increase in production capacity is essential. This can be achieved through efficient assembly orchestrations based on continuous assembly progress detection. This paper contributes to the necessary data acquisition during the assembly process. Various methods from the state of the art are presented, evaluated and compared against each other. Finally, a modular concept for data acquisition on the construction site of electrolysis plants based on data fusion is proposed.

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