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008			231219s2024 sz | o |||| 0|eng d
020			_a9783031474446
024	7		_a10.1007/978-3-031-47444-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aT55.4-60.8 _b2024 EB
100	1		_aAbonyi, J�anos. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aOntology-Based Development of Industry 40 and 50 Solutions for Smart Manufacturing and Production : _bKnowledge Graph and Semantic Based Modeling and Optimization of Complex Systems _cby J�anos Abonyi, L�aszl�o Nagy, Tam�as Ruppert
250			_a1st ed. 2024.
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en li��nea
336			_atexto _btxt _2rdacontent
337			_aelectro��nico _bc _2rdamedia
338			_arecurso electro��nico _bcr _2rdacarrier
490	0		_aSpringer Series in Advanced Manufacturing _x2196-1735
505	0		_aPart I Introduction and motivation of the book -- Introduction to the industrial application of semantic technologies -- Ontology-based modeling of a wire harness manufacturing processes -- Knowledge graph-based framework to support human-centered collaborative and ergonomic manufacturing in Industry 5.0 -- Part II Problem statement of network science-based process optimization -- Analytic hierarchy process and multilayer network-based method for assembly line balancing -- Efficient network community detection algorithm based on crossing minimization and bottom-up segmentation -- Hypergraph-based analysis of collaborative manufacturing -- Cookbook for semantic-based modeling and optimization of manufacturing systems -- Conclusion.
520			_aThis book presents a comprehensive framework for developing Industry 4.0 and 5.0 solutions through the use of ontology modeling and graph-based optimization techniques. With effective information management being critical to successful manufacturing processes, this book emphasizes the importance of adequate modeling and systematic analysis of interacting elements in the era of smart manufacturing. The book provides an extensive overview of semantic technologies and their potential to integrate with existing industrial standards, planning, and execution systems to provide efficient data processing and analysis. It also investigates the design of Industry 5.0 solutions and the need for problem-specific descriptions of production processes, operator skills and states, and sensor monitoring in intelligent spaces. The book proposes that ontology-based data can efficiently represent enterprise and manufacturing datasets. The book is divided into two parts: modelingand optimization. The semantic modeling part provides an overview of ontologies and knowledge graphs that can be used to create Industry 4.0 and 5.0 applications, with two detailed applications presented on a reproducible industrial case study. The optimization part of the book focuses on network science-based process optimization and presents various detailed applications, such as graph-based analytics, assembly line balancing, and community detection. The book is based on six key points: the need for horizontal and vertical integration in modern industry; the potential benefits of integrating semantic technologies into ERP and MES systems; the importance of optimization methods in Industry 4.0 and 5.0 concepts; the need to process large amounts of data while ensuring interoperability and re-usability factors; the potential for digital twin models to model smart factories, including big data access; and the need to integrate human factors in CPSs and provide adequate methods tofacilitate collaboration and support shop floor workers.
942			_2lcc _cLE
988			_aSpringer_Engineering_2024
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-47444-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
999			_c401891 _d401891