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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 36972 条记录，以下是201-210 订阅

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Concept for Value Stream-Oriented Analyses of Event-based data in Three Perspectives

Concept for Value Stream-Oriented Analyses of Event-based Da...

引用

2023 international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Scheder, Noel Teriete, Tim Eisl, Stefanie Nausch, Mathias Bohm, Markus Fraunhofer Austria GmbH Assembly Planning and Assistance Systems Vienna Austria Fraunhofer Institute for Manufacturing Engineering and Automation Ipa Factory Planning and Value Stream Design Stuttgart Germany

ISBN: (纸本)9781665474511

Value stream mapping is an established method for analyzing and optimizing manufacturing systems. However, because of their high complexity and dynamic behavior, modern production systems of the industry 4.0 era are increasingly difficult to model in a static value stream map. A general approach to handle this challenge is to digitalize analysis, modeling, and visualization. This study shows how information gathered digitally during value stream analysis can be structured in a way to provide added value for users in visualization. For this purpose, three perspectives are derived and described in detail - 'value stream map', 'order tracking' and 'resource monitoring'. These perspectives are also intended to cover sustainability KPIs and be real-time capable. The approach exploits standardized event data to ensure processing and development. It contributes to the utilization of data in the manufacturing context. © 2023 IEEE.

关键词： data visualization

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An Efficacy Analysis of data using Fuzzy Logic and Fractal Encryption Techniques for Cloud Platform data Security

An Efficacy Analysis of Data using Fuzzy Logic and Fractal E...

引用

2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Dineshkumar, P. Jeeva, V. Nithiesh, C. Arun, P.J. Kumar, K. Sathish K. S. Rangasamy College of Technology Department of Information Technology Tiruchengode637 215 India Sri Sai Ram Engineering College Computer Science and Business Systems Chennai600044 India

ISBN: (纸本)9798350359688

In the realm of cloud computing, safeguarding data confidentiality is paramount. This paper introduces a novel approach to data security in cloud platforms, merging fuzzy logic and fractal encryption techniques. Fuzzy logic accommodates uncertain data, enabling adaptable encryption strategies tailored to cloud dynamics. Fractal encryption harnesses self-similarity in fractals to create intricate encryption keys, fortifying data protection. Together, they propose a robust solution to mitigate unauthorized access, data breaches, and cyber threats. Through theoretical exploration and practical testing, the efficacy of this approach is demonstrated, highlighting its potential to address cloud security challenges. The scalability and versatility of these methods make them applicable across diverse cloud infrastructures, ensuring comprehensive data protection. This paper advocates for the adoption of fuzzy logic and fractal encryption as proactive measures to enhance cloud platform security, fostering trust among users and stakeholders. © 2024 IEEE.

关键词： Cloud platforms

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Multimodal Learning in Natural Language Processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed College of Business Westcliff University CA United States College of Technology & Engineering Westcliff University CA United States University Canada West Vancouver Canada Gus Institute Global University Systems London United Kingdom Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (纸本)9798331509828

Multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. Applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency. © 2025 IEEE.

关键词： knowledge graph

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Building a Sustainable knowledge Management System from Dark data in Industrial Maintenance 18th

Building a Sustainable Knowledge Management System from Dark...

引用

18th international conference on knowledge Management in Organisations (KMO)

作者： Zhong, Keyi Jackson, Tom West, Andrew Cosma, Georgina Loughborough Univ Sch Business Epinal Way Loughborough LE11 3TU Leics England Loughborough Univ Sch Mech Elect & Mfg Engn Epinal Way Loughborough LE11 3TU Leics England Loughborough Univ Sch Comp Sci Epinal Way Loughborough LE11 3TU Leics England

ISBN: (纸本)9783031632686;9783031632693

As digitalization exponentially generates vast data volumes, concerns about its environmental impact have surged. Dark data, characterized by being stored but underutilized, not only presents challenges to organizations but also aligns with the imperative of digital decarbonization. Notably, the manufacturing domain lacks prior exploration of addressing dark data issues. In response, this study introduces a knowledge management perspective to tackle the challenge through the construction of a knowledge Management System (KMS). The proposed KMS is built upon the foundation of a knowledge Graph (KG). data ingestion analysis reveals that the identified data sources are sparse and incomplete, prompting the application of data scraping and enrichment techniques. data from various sources is integrated, and knowledge is extracted from the enriched datasets. A fault KG containing the physical level and the failure level is manually constructed to ensure enhanced accuracy and reliability. The proposed KMS framework, grounded in the power of KGs, serves as a comprehensive solution to the challenges posed by dark data in the manufacturing sector, and provides industrial maintenance applications such as fault analysis and decision-making guidance, to improve knowledge reuse and promote digital decarbonization.

关键词： Dark data knowledge Management systems (KMS) Digitalization Industrial Maintenance

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AI Implementation and Capability Development in manufacturing: An Action Research Case 57

AI Implementation and Capability Development in Manufacturin...

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57th Hawaii international conference on System Sciences (HICSS)

作者： Eklof, Jon Snis, Ulrika Lundh Hamelryck, Thomas Grima, Alexander Ronning, Ola Univ Copenhagen Copenhagen Denmark GKN Aerosp Trollhattan Sweden Univ West Trollhattan Sweden

ISBN: (纸本)9780998133171

This action research article presents a case study of a global manufacturing company deploying artificial intelligence (AI) to develop capabilities and enhance decision-making. This study explores considerations and trade-offs involved in introducing AI into daily operations, leading up to the decision to develop AI capabilities in-house or outsource them. The case study offers in-depth technical descriptions of model selection, dataset creation, model adoption, model training and evaluation while addressing organizational obstacles and decision-making processes. The study's findings highlight the importance of collaboration between technical experts, business leaders, and end-users, as well as the interaction and collaboration between AI systems and human employees in the workplace. The article contributes a practical perspective on AI implementation in manufacturing, emphasizing the need to balance in-house capability development with external acquisition. Although the case study company managed to create an in-house model, factors such as implementation, debugging, data requirements, training time, and performance led to outsourcing the capabilities. However, making this informed decision required capabilities and insights that were acquired through practical work. Consequently, although in-house development can be challenging, it can also enhance organizational capabilities and provide the necessary knowledge to make informed decisions about future development or outsourcing.

关键词： AI capability development AI implementation AI in manufacturing

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SINDIT: A Framework for knowledge Graph-Based Digital Twins in Smart manufacturing 1st

SINDIT: A Framework for Knowledge Graph-Based Digital Twins...

引用

1st international Workshop on the Design, Verification, and Validation of IoT systems, GRAAL4IoT 2024 and 1st international Workshop on IoT Standards, STAND4IoT 2024, held in conjunction with 7th IFIP WG 5.5 international Cross-Domain conference on Internet of Things, IFIPIoT 2024

作者： Lam, An Ngoc Svaland, Gøran Brekke Barcelona, Miguel Ángel Keaveney, Shane Mallouli, Wissam Nguyen, Luong Belbachir, Assia Ma, Xiang Srivastava, Akhilesh Kumar Belbachir, Ahmed Nabil SINTEF AS Oslo Norway Instituto Tecnológico de Aragón Zaragoza Spain Croom Medical Croom Co. Limerick Ireland Montimage Paris France NORCE Norwegian Research Centre Grimstad Norway

ISBN: (纸本)9783031820649

Digital twins are revolutionizing smart manufacturing by facilitating real-time monitoring, simulation, and optimization of physical processes. This paper introduces the SINDIT framework, a comprehensive approach tailored for developing knowledge graph-based digital twins. By seamlessly integrating cognitive capabilities, SINDIT enhances decision-making and operational efficiency within manufacturing systems. Central to its architecture is a robust data pipeline, adept at organizing and linking vast amounts of heterogeneous data, thereby enabling advanced data analytics and reasoning. Case studies from the pilots of the COGNIMAN project underscore the practical utility and benefits of the SINDIT framework. These studies showcase notable enhancements in predictive maintenance, process optimization, and overall productivity. By harnessing the power of knowledge graphs and cognitive capabilities, SINDIT represents a promising avenue for driving innovation and efficiency in smart manufacturing. Through this framework, manufacturers can achieve a higher level of operational insight and agility, leading to improved performance and competitiveness in the market. © IFIP international Federation for Information Processing 2025.

关键词： Smart manufacturing

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A SIMULATED EVALUATION OF POWDER FLOWABILITY THROUGH A PARTIALLY OBSTRUCTED CONSUMABLE IN BLOWN POWDER DIRECTED ENERGY DEPOSITION systems 19

A SIMULATED EVALUATION OF POWDER FLOWABILITY THROUGH A PARTI...

引用

ASME 19th international manufacturing Science and engineering conference (MSEC) held jointly with the 52nd North American Research conference (NAMRC)

作者： DeWitte, Lisa Fu, Katherine Georgia Inst Technol Atlanta GA 30332 USA Univ Wisconsin Madison Madison WI USA

ISBN: (纸本)9780791888100

In the interest of continued industrialization of metal additive manufacturing in modern production environments, cost is often referenced as a primary deterrent to new adopters. Conventional economic models for additive systems, processes, and supply chains often focus on specific process applications with little generalizability, or they neglect significant costs associated with production such as machine maintenance and consumable part replacement. Compounding the latter issue are substantial knowledge gaps in consumable part wear characterization for additive and other convergent manufacturing systems. In coaxial blown powder directed energy deposition systems, gas atomized metal powder is wasted during material deposition at a rate that is partly dependent on present wear phenomena in a consumable nozzle housed in the cladding head assembly. The price and lead time required to replace the nozzle incentivizes its reuse even when visibly worn. Often this initiates a process quality decline in the form of underbuilt geometry and internal defects due to losses in powder catchment efficiency. While depositing H13 steel using a hybrid manufacturing machine tool equipped with such a deposition system, a unique partial clog with a bridge-like structure formed at the consumable nozzle exit when supporting argon gas flows failed mid-process. To further understand coaxial multi-phase powder flow in the event of support gas failure, a computational fluid dynamics simulation is tailored to relevant process parameters, H13 powder material profile, and machine operator observations collected after the incident. The resulting differences in powder flow compared to control gas flow parameters is presented and discussed. The powder flowability and performance of the clogged nozzle is then assessed by using an optical profilometer to extract the profile of the clog and recreate the clog geometry within the simulation environment. In past work this simulation has been experimental

关键词： Blown Powder Directed Energy Deposition Hybrid manufacturing Computational Fluid Dynamics

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Bridging Domain Expertise and AI through data Understanding 29

Bridging Domain Expertise and AI through Data Understanding

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29th ACM Annual international conference on Intelligent User Interfaces (IUI)

作者： Holstein, Joshua Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798400705090

With the ongoing digitalization of complex systems, for example in manufacturing, domain experts' detailed understanding of datasets is pivotal to effectively training machine learning (ML) models. This understanding obtained through their deep domain knowledge, enables domain experts to collaborate with method experts to identify deficiencies in datasets, such as biases or anomalies, and curate them. Such curated datasets build the foundation for training effective ML models, which are able to inform subsequent decision-making processes. However, understanding the increasingly large and complex datasets and systems they represent is challenging. Therefore, this doctoral thesis investigates methods to support domain experts in building a solid data understanding for complex datasets. Specifically, the thesis focuses on three key areas: conceptualizing data understanding, augmenting domain knowledge through VIS4ML systems to curate datasets, and providing contextual information for AI-assisted decision-making. Initial findings indicate that VIS4ML systems effectively support domain experts in understanding and contextualizing datasets, enabling them to curate datasets collaboratively. This understanding, particularly when enriched through contextual information, shows promise in enhancing AI-assisted decision-making.

关键词： data Understanding Human-AI Decision Making VIS4ML Domain Experts

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Curriculum for a New Five-Year Academic Program in Intelligent systems engineering and Software engineering

Curriculum for a New Five-Year Academic Program in Intellige...

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Solo, Ashu M. G. Maverick Trailblazers Inc Wilmington DE 19805 USA

ISBN: (纸本)9798350361513;9798350372304

This research paper proposes a curriculum for a five-year academic program with a bachelor's degree (honors) in intelligent systems engineering and software engineering and a master's degree in intelligent systems engineering and software engineering. This program includes courses in data structures, compiler design, operating system design, firmware design, database systems, computer graphics and virtual reality design, static and dynamic website design, development of chatbots and voice assistants, software engineering methodology, knowledge-based systems, fuzzy logic, neural networks, evolutionary computation, evolutionary multiojective optimization, machine learning, image processing, computer vision, pattern recognition, voice recognition, natural language processing, data science, control systems, intelligent control systems, robotics, digital signal processing, mathematics, engineering physics, biology, etc. These degrees will allow graduates to have a good understanding of all of the main branches of intelligent systems engineering and software engineering as well as other relevant subjects in electrical and computer engineering.

关键词： education intelligent systems computational intelligence soft computing artificial intelligence machine learning software development firmware development

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Applying Mechanistic AI for Accessing the Production Technology Flexibility Potential 15

Applying Mechanistic AI for Accessing the Production Technol...

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15th international conference on Mechanical and Intelligent manufacturing Technologies (ICMIMT)

作者： Mosia, Ngaka Univ South Africa Dept Ind Engn Johannesburg South Africa

ISBN: (纸本)9798350362664;9798350362671

Machine control systems have undergone a significant transformation, transitioning from centralized computer numeric control to automatic control. One of the key challenges in this evolution stems from the complex and varied nature of many manufacturing systems, characterized by multiscale, Multiphysics, dynamic, and stochastic elements. This complexity has spurred numerous innovations at the convergence of artificial intelligence (AI), data analytics, and manufacturing sciences. Consequently, the question of how the manufacturing industry can leverage the technology flexibility available is addressed through the inherent adaptive capabilities of AI. This research aims to articulate and establish a process for imbuing flexibility into manufacturing processes, leveraging the technology of the manufacturing system with the integration of AI. The objective is to harness the adaptive potential of technology-based flexibility using AI, employing a combination of AI methods, mechanism-driven principles, and engineering technology. In this context, flexibility denotes a manufacturing process's ability to respond effectively to new system requirements, whether planned or unforeseen. The paper asserts that the value of system flexibility is an intrinsic advantage of manufacturing system technology. A qualitative research approach is employed to investigate and elucidate how the manufacturing system can be modeled to unlock its flexibility potential, utilizing Mechanistic AI applications.

关键词： manufacturing technology AI automation FMS

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