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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Maier, Florian A. Puppala, Sivaphani Oberle, Michael Software Defined Energy-Cell Manufacturing Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Center for Digitalized Battery Cell Manufacturing Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Centre for Digital Tools in Manufacturing Stuttgart Germany

ISBN: (纸本)9798350344349

This review provides a structured literature analysis of Artificial Intelligence (AI) applications in enhancing manufacturing resilience. The research is guided by three primary questions addressing the use cases, technologies, and benefits of AI across the five resilience phases: Prepare, Prevent, Protect, Respond, and Recover. Findings from 78 papers reveal that AI significantly contributes to predictive maintenance, risk mitigation, and quality control, with machine learning and deep learning being the predominant technologies. The study highlights the pivotal role of AI in advancing manufacturing towards proactive, resilient, and adaptable operations. The insights gleaned offer a roadmap for future research and practical AI integration in manufacturing, underscoring the value of AI in driving industrial innovation and efficiency. © 2024 IEEE.

关键词： Fault detection

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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

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International Journal of systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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COMBINING SIMULATION AND RECURRENT NEURAL NETWORKS FOR MODEL-BASED CONDITION MONITORING OF MACHINES

COMBINING SIMULATION AND RECURRENT NEURAL NETWORKS FOR MODEL...

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2024 Winter Simulation Conference, WSC 2024

作者： Wuttke, Alexander Rabe, Markus Hunker, Joachim Diepenbrock, Jan-Philipp Dept. IT in Production and Logistics TU Dortmund University Dortmund Germany Fraunhofer Institute for Software and Systems Engineering Dortmund Germany IVA Schmetz GmbH Menden Germany

ISBN: (纸本)9798331534202

Maintenance is pivotal in industry, with condition-based maintenance emerging as a key strategy. This involves monitoring the machine condition through sensor data analysis. Model-based approaches compare observed data with expected values from models, which requires high-quality models. An established method is to use simulation models, which in many cases produce good results but may lack precision due to uncertainties. Alternatively, models created by machine learning can detect patterns directly from data. This paper proposes combining simulation models with machine learning models, leveraging the simulation’s a-priori knowledge and machine learning’s data patterns to enhance models for condition monitoring. Recurrent neural networks are suggested as the machine learning method. The paper outlines a systematic approach and demonstrates its application in an industrial use case, which investigates vacuum processes in industrial furnaces. © 2024 IEEE.

关键词： Neural network models

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Inherent Strain Modelling and Neutron Diffraction Stress Analyses in Invar- 10wt%TiCN Manufactured by Laser Powder Bed Fusion

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Lasers in Manufacturing and Materials Processing 2025年 1-28页

作者： Oyedeji, Ayodeji Nathaniel Sacks, Natasha Venter, Andrew Marais, Deon Pötschke, Johannes Department of Industrial Engineering Stellenbosch University Stellenbosch7602 South Africa SOC Limited Pretoria South Africa Fraunhofer Institute for Ceramic Technologies and Systems IKTS Dresden Germany

In this study, the residual stress state of an Invar-10wt%TiCN composite was evaluated through experimental non-destructive neutron diffraction (ND) of the FeNi phase and inherent strain modelling (ISM) simulations. Comparisons were made to the stress state of FeNi in Invar to investigate the degree of change in the alloy due to the addition of the TiCN. The materials were fabricated using laser powder bed fusion (LPBF) according to a full factorial Central Composites Design of Experiments approach to develop the optimal process parameters. High densities of 99.87% for Invar and 99.28% for Invar-10wt%TiCN were achieved, with the TiCN addition leading to a hardness improvement of approximately 71.8%. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses were done to study the microstructure. The TiCN resulted in a high distortion of 113% in the ISM cantilever sample due to its higher coefficient of thermal expansion. Close agreement was found between the ISM simulation and the ND experimental residual stress results. The stresses are predominantly compressive in the interior and tensile on the surfaces. This agreement enabled extrapolations of the estimated stresses in the near-surface regions. Magnitudes are dependent on the stress component considered and build position. Grain refinement in the Invar-TiCN composite enhanced the compressive residual stresses. © The Author(s) 2025.

关键词： Grain refinement

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Targeted Data Generation in the Continuous Production of Anode Slurries for Lithium-Ion Battery Cells 6

Targeted Data Generation in the Continuous Production of Ano...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Oberdiek, Sven Jalowy, Leah Vazquez, Flavio Gonzalez Risling, Monika Wahl, Katja Quality and Reliability Management Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Dispersing Technology Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Purity-specific Automation Systems Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Stuttgart Germany Data and Application Services for Digital Production Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany

ISBN: (纸本)9798350344349

This research paper explores the transition from batch mixing to continuous mixing processes for the production of slurries used in lithium-ion battery cells. The conventional batch mixing methods, prevalent in European industry, suffer from time-consuming cleaning processes, lengthy mixing times, and the inability to monitor paste conditions in real-Time. The study proposes a solution using continuous mixing with a twin-screw extruder, highlighting advantages such as reduced cleaning time, immediate sample characterization, and minimized production risks. The research aims to develop a test setup and procedure to accelerate the understanding of the continuous mixing process, leveraging minimal resources and time, and facilitating the training of artificial intelligence algorithms. The ultimate goal is to create a digital twin encompassing all influencing factors for predictive and prescriptive analytics. The methodology involves literature reviews, expert surveys, and experimental setups, with an emphasis on inline measuring devices. The paper presents results related to relevant product characteristics, influencing factors, and the selection of suitable measuring equipment. The experimental setup includes a database structure, human-machine interface, and visualization of measurement data. The study concludes with insights into the correlation analysis of influencing factors and product characteristics, emphasizing the need for further experiments and the development of algorithms for predictive quality assessments in continuous mixing processes. © 2024 IEEE.

关键词： Lithium-ion batteries

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Flexible assembly planning for self-organizing production cells 6

Flexible assembly planning for self-organizing production ce...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lehner, Christian Kosak, Oliver Ponsar, Hella Reif, Wolfgang Institute for Software & Systems Engineering University of Augsburg Universitätsstraße 2 Augsburg86159 Germany

In the era of Industry 4.0 and individualized mass customization, the demand for adaptable manufacturing systems is paramount. Therefore, assembly plans used in such systems must also allow a high degree of flexibility to adapt to changing requirements and conditions. At the same time, they must still guarantee the successful execution of the assembly process. In this paper, we present a novel approach to modeling assembly plans in the context of an ongoing, larger project in which we intend to implement flexible manufacturing in a real production cell. In our assembly plans, we specifically emphasize flexibility regarding the order of assembly steps, the allocation of appropriate teams of robots to execute these steps with applicable parts, and the ability to respond to failures in the system. We present a comprehensible modeling of flexible assembly plans, accompanied by the semantics of Petri nets, illustrated by example, and comparatively evaluated against other approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Flexible manufacturing systems

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Municipal AI integration: a structured approach

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Frontiers of Urban and Rural Planning 2025年第1期3卷 1-11页

作者： Eichholz, Lutz Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany

This research aims to develop a structured approach for implementing Artificial Intelligence (AI) in municipal governance. The study addresses three key questions: (1) What principles can be derived from existing AI implementation frameworks? (2) How should an approach for municipal AI projects be designed? (3) What are the main risks at each implementation stage? The research methodology combined three components: (1) a literature review of AI and software implementation approaches and municipal challenges, (2) analysis of findings from long-term collaborations with German municipalities and two specific AI implementation projects, and (3) low-threshold validation through two webinars with municipal representatives. The study produced an eight-phase implementation framework emphasizing iterative experimentation and risk awareness, while highlighting the distinct challenges of AI compared to traditional software implementation. Key phases include task identification, AI suitability assessment, data evaluation, solution development/procurement, MVP creation, testing, operational transition, and continuous monitoring. Each phase incorporates AI-specific steps and risk factors tailored to municipal contexts. While the framework provides practical guidance for municipal AI implementation, positioning cities for the gradual transition toward post-smart cities with AI-enabled governance, its current foundation primarily reflects German municipal experiences. Further research and case studies are needed to validate and adapt the framework for diverse global contexts.

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Challenges and Opportunities for Enabling the Next Generation of Cross-Domain Dataspaces 2

Challenges and Opportunities for Enabling the Next Generatio...

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2nd International Workshop on Semantics in Dataspaces, SDS 2024

作者： Deshmukh, Rohit A. Collarana, Diego Gelhaar, Joshua Theissen-Lipp, Johannes Lange, Christoph Arnold, Benedikt T. Curry, Edward Decker, Stefan Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany RWTH Aachen University Aachen Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany Insight Centre for Data Analytics University of Galway Galway Ireland

Dataspaces are regarded as a standardized solution for sharing data in a trusted way. However, providing and sharing high-quality data across dataspaces poses several scientific and technical challenges, opening new research avenues. Developing governance models and technologies for supporting cross-domain integration of data and services from the existing single-domain dataspaces represents a significant challenge. In this vision paper, we discuss the challenges for enabling next-generation dataspaces and propose an innovative approach that aims at developing a vision and identifying requirements and building blocks for next-generation dataspaces, followed by defining a roadmap and practical migration paths for the existing dataspaces towards the next-generation dataspaces. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Interoperability

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Phase Re-Service in Reinforcement Learning Traffic Signal Control 27

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhiyao Gunter, George Quinones-Grueiro, Marcos Zhang, Yuhang Barbour, William Biswas, Gautam Work, Daniel Institute for Software Integrated Systems Vanderbilt University Department of Civil and Environmental Engineering United States Institute for Software Integrated Systems Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798331505929

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation. © 2024 IEEE.

关键词： Markov processes

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A data quality assessment tool for agricultural structured data as support for smart farming 43

A data quality assessment tool for agricultural structured d...

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43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food systems: Challenges and Solutions, GIL 2023

作者： Schroth, Christof Kelbert, Patricia Vollmer, Anna Maria Fraunhofer Institute for Experimental Software Engineering IESE Department Data Science Fraunhofer-Platz 1 Kaiserslautern67663 Germany

ISBN: (纸本)9783885797241

In the field of precision farming or smart farming, more and more sensors are used and produce a massive amount of data. Examples are machinery, weather stations, or georeferenced data, which can be used, among other things, by Artificial Intelligence decision support systems to improve or facilitate farmers’ daily work tasks. Even if there are no issues in transferring (Internet of Things) sensor data from machines to farm management information systems, data still contain errors such as missing, implausible, or incorrect data values. In this paper, we present an automated data quality assessment (DQA) tool based on the ISO25012 standard. We describe the process of how we developed this tool with support from practitioners who produce agricultural data in the context of the EU Horizon 2020 project DEMETER. Additionally, we highlight some of the requirements we collected for such a tool and briefly discuss how we addressed them. For example, we learned that in the context of developing smart farming services, the data quality dimensions Accuracy, Completeness, Consistency, and Credibility are the most important ones for practitioners such as farmers, digital service providers, or machine suppliers. Therefore, we included them in the DQA tool and implemented it in Python. It is released under the open-source Apache 2 license. Individual parameters can be provided as input for calculations (e.g., thresholds or time lengths) to meet different users’ needs. The output of the DQA is provided in machine-readable JSON format and can be used for further analysis, e.g., to improve the quality of the data collection or the followup data analysis. This can help practitioners develop more valuable smart farming services. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Decision support systems

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