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2024 | Buch

Introduction and Motivation of the Book Kapitel lesen Erstes Kapitel lesen

Ontology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production

Knowledge Graph and Semantic Based Modeling and Optimization of Complex Systems

verfasst von: János Abonyi, László Nagy, Tamás Ruppert

Verlag: Springer Nature Switzerland

Buchreihe : Springer Series in Advanced Manufacturing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This 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: modeling and 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 to facilitate collaboration and support shop floor workers.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction and Motivation of the Book
Abstract
The paramount importance of proficient information management for the progression of manufacturing processes, particularly within the purview of smart manufacturing, necessitates suitable modeling and comprehensive examination of crucial, interacting elements. The objective of this book is to propose a framework predicated on an ontology model for the development of Industry 4.0 solutions, coupled with an array of techniques dedicated to the graph-based optimization of manufacturing data. The framework and these techniques are compartmentalized into sections on modeling and optimization respectively.
János Abonyi, László Nagy, Tamás Ruppert

Semantic Modeling—Ontologies and Knowledge Graphs

Frontmatter
Chapter 2. Introduction to the Industrial Application of Semantic Technologies
Abstract
Adequate information management is critical for the development of manufacturing processes. Therefore, this chapter aims to provide a systematic overview of ontologies that can be utilized in building Industry 4.0 applications and highlights that ontologies are suitable for manufacturing management. Additionally, industry-related standards and other models are also discussed.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 3. Data Sharing in Industry 4.0—AutomationML, B2MML and International Data Spaces-Based Solutions
Abstract
The concept of a data ecosystem and Industry 4.0 requires high-level vertical and horizontal interconnectivity across the entire value chain. Its successful realisation demands standardised data models to ensure transparent, secure and widely integrable data sharing within and between enterprises. This chapter provides a PRISMA method-based systematic review about data sharing in Industry 4.0 via AutomationML, B2MML and International Data Spaces-based solutions. The interconnection of these data models and the ISA-95 standard is emphasised. This review describes the major application areas of these standards and their extension as well as supporting technologies and their contribution towards horizontal integration and data ecosystems. This review highlights how much value interconnected, exchanged and shared data gained in recent years. Standardized data sharing mechanisms enable real-time, flexible and transparent communication, which features became top requirements to gain a competitive advantage. However, to foster the shift from within company data communication towards the data ecosystem, IT- and people-oriented cultures must be well-established to ensure data protection and digital trust. The review of these standardized data exchange and sharing solutions in this chapter can contribute to the development and design of Industry 4.0-related systems as well as support related scientific research.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 4. Ontology-Based Modeling of a Wire Harness Manufacturing Processes
Abstract
This chapter aims to verify that ontology-based modeling can be utilized to create structured and contextualized models that can support the development of the manufacturing process. The applicability of ontology-based process modeling and data analysis is demonstrated on a wire-harness assembly-based benchmark, where semantic modeling and data query analysis was performed.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 5. Knowledge Graph-Based Framework to Support the Human-Centric Approach
Abstract
This chapter proposes the Human-Centric Knowledge Graph (HCKG) framework by adapting ontologies and standards that can model the operator-related factors such as monitoring movements, working conditions or collaboration with robots. Furthermore, graph-based data queries, visualization and analytics are also presented in the form of an industrial case study. The main contribution of this work is a knowledge graph-based framework, where the work performed by the operator is of concern, including the evaluation of movements, collaboration with machines, ergonomics and other conditions. Additionally, utilization of the framework is demonstrated in a complex assembly line-based use case, by applying examples of resource allocation and comprehensive support concerning collaboration between the shop-floor workers and ergonomic aspects. The importance of highly monitored and analyzed processes connected by information systems such as knowledge graphs is increasing. Moreover, the integration of operators has also become urgent due to their high costs and from a social point of view. An adequate framework to implement the Industry 5.0 approach requires effective data exchange in a highly complex manufacturing network to utilize resources and information. Furthermore, the continuous development of collaboration between human and machine actors is fundamental for Industrial Cyber-Physical Systems, as the workforce is one of the most agile and flexible manufacturing resources.
János Abonyi, László Nagy, Tamás Ruppert

Network Science-Based Process Optimization-Advanced Manufacturing Analytics

Frontmatter
Chapter 6. Problem Statement of Network Science-Based Process Optimization
Abstract
The previous part of this book showed a variety of applications and highlighted the advantages of semantic technologies in modern industry. Following the advised graph-based data access approach, this chapter aims to give an overview of some of the possible analytic methods and optimization procedures that can be utilized on graph networks. The motivation is to provide effective optimization for complex production processes of an Industry 4.0 environment and to handle the dynamically changing conditions and requirements on a shop floor.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 7. Analytic Hierarchy Process and Multilayer Network-Based Method for Assembly Line Balancing
Abstract
This chapter introduces a novel, combined analytic hierarchy process and multilayer network-based method for assembly line balancing. Assembly line balancing improves the efficiency of production systems by the optimal assignment of tasks to operators. The optimization of this assignment requires models that provide information about the activity times, constraints and costs of the assignments. A multilayer network-based representation of the assembly line-balancing problem is proposed, in which the layers of the network represent the skills of the operators, the tools required for their activities and the precedence constraints of their activities. The activity-operator network layer is designed by a multi-objective optimization algorithm in which the training and equipment costs as well as the precedence of the activities are also taken into account. As these costs are difficult to evaluate, the analytic hierarchy process (AHP) technique is used to quantify the importance of the criteria. The optimization problem is solved by a multi-level SA algorithm, that efficiently handles the precedence constraints.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 8. Network Community Detection Algorithm for Graph Networks
Abstract
This chapter presents how communities in networks can be detected by integrating barycentric serialization with bottom-up segmentation. Because nodes are efficiently ordered according to their neighbors by barycentric serialization, the segmentation algorithm provides modules in a computationally more efficient manner than the most frequently used Louvain community detection algorithms. The approach ensures efficient community detection by merging adjacent nodes or segments in a way that maximizes modularity, eliminating the need to test the entire dataset, and thus reducing iteration costs. Furthermore, the method is capable of accurately determining the number of communities in a network. The efficiency of the method is compared with other community detection algorithms based on benchmark problems.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 9. Hypergraph-Based Analysis of Collaborative Manufacturing
Abstract
This chapter aims to present a hypergraph-based analysis method. The design of these Operator 4.0 solutions requires a problem-specific description of manufacturing systems, the skills, and states of the operators, as well as of the sensors placed in the intelligent space for the simultaneous monitoring of the collaborative work. The design of a collaborative manufacturing requires the systematic analysis of the critical sets of interacting elements. The proposal is that hypergraphs can efficiently represent these sets, moreover, studying the centrality and modularity of the resultant hypergraphs can support the formation of collaboration and interaction schemes and the formation of manufacturing cells. The main finding of this chapter is that the development of these solutions can be applied in collaborative manufacturing. Collaborative manufacturing aims to achieve real-time monitoring-based control for semi-automated production systems, thereby creating more precise collaboration between human workers and machines. The key idea is that hypergraphs can efficiently represent these sets, moreover, studying the centrality as well as modularity of the resultant hypergraphs can support the formation of collaboration and interaction schemes in addition to the creation of manufacturing cells.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 10. Source List for Semantic-Based Modeling, Utilization of Graph Databases and Graph-Based Optimization of Manufacturing Systems
Abstract
This Chapter aims to serve as a source list for researchers and engineers interested in the topic of ontology-based modeling and optimization. Each tools are explained briefly and the sources for the software tools are provided.
János Abonyi, László Nagy, Tamás Ruppert
Chapter 11. Conclusions
Abstract
The previous chapters discussed the theoretical and practical results of this study. The present chapter aims to summarize the contributions made to the research of ontology-based development of Industry 4.0 and 5.0 solutions.
János Abonyi, László Nagy, Tamás Ruppert
Backmatter
Metadaten
Titel
Ontology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production
verfasst von
János Abonyi
László Nagy
Tamás Ruppert
Copyright-Jahr
2024
Electronic ISBN
978-3-031-47444-6
Print ISBN
978-3-031-47443-9
DOI
https://doi.org/10.1007/978-3-031-47444-6

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