Ontology-Based Development of Industry 4.0 and 5.0 Solutions for Smart Manufacturing and Production: Knowledge Graph and Semantic Based Modeling and ... (Springer Series in Advanced Manufacturing)

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: 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. Preface Contents 1 Introduction and Motivation of the Book 1.1 Introduction of the Research Topics—Problem Statement 1.1.1 Requirements and Data Integration of Industry 4.0 Systems 1.1.2 Standards and Ontology-Based Modeling of Manufacturing 1.1.3 Production Models and Digital Twins 1.1.4 Human-Centric and Collaborative Approach—Challenges of Industry 5.0 1.1.5 Problem Statement of the Book 1.2 Proposed Framework for Ontology-Based Development ... 1.3 Research Questions References Part I Semantic Modeling—Ontologies and Knowledge Graphs 2 Introduction to the Industrial Application of Semantic Technologies 2.1 Ontologies and Semantic Models in General 2.2 Industry Standard-Based Representation of Manufacturing 2.3 Semantic Modeling, Ontologies and Description Methods ... 2.4 Ontologies and Semantic Models for MES Development 2.5 Semantic Representations of Sensory Data 2.6 Product-Process-Resource Modeling and Workflow 2.7 Semantic Technologies and Metrics to Describe and Support the Operator 2.7.1 Human Activity Recognition 2.7.2 Ergonomics and Collaboration 2.7.3 Metrics to Evaluate Human-Machine Interactions 2.8 Ontology-Based Analysis and Solutions in Manufacturing Systems 2.9 Comparison of Ontology-Based Methods ... References 3 Data Sharing in Industry 4.0—AutomationML, B2MML and International Data Spaces-Based Solutions 3.1 Introduction 3.2 Overview of the Interaction Between Standard Data Models 3.3 Research Method 3.4 Systematic Review of Standard Data Models ... 3.4.1 Overview of Automation Markup Language (AutomationML) 3.4.2 Overview of the Business to Manufacturing Markup Language (B2MML) 3.4.3 Overview of the International Data Spaces (IDS) 3.5 Discussion 3.6 Conclusion References 4 Ontology-Based Modeling of a Wire Harness Manufacturing Processes 4.1 Applied Software Tools of Ontology-Based Modeling 4.2 Ontology Modeling—Creation of Manufacturing Based Knowledge Graph 4.3 Data Queries and Evaluation of Ontology Data 4.4 Summary of the Ontology-Based Modeling of a Manufacturing Process References 5 Knowledge Graph-Based Framework to Support the Human-Centric Approach 5.1 State of the Art—Knowledge Gap and Motivation 5.2 Human-Centered Knowledge Graph Towards Collaboration in Manufacturing 5.2.1 Manufacturing Operations Management 5.2.2 Monitoring System Concept 5.2.3 Design Structure of the HCKG Concept 5.3 Human-Robot Collaboration and Key Performance Indicators 5.4 Applied Methodologies and Software Tools 5.5 Development of the Industry-Specific Human-Centered Knowledge Graph 5.6 Discussion on KG-Based Analytics of the Use Case 5.7 Summary of Human-Centric Knowledge Graph Framework References Part II Network Science-Based Process Optimization-Advanced Manufacturing Analytics 6 Problem Statement of Network Science-Based Process Optimization 6.1 Application of Semantic Features for Optimization 6.2 Convert Data into Graph Network and Multilayer Network Representation 6.3 Algorithmic Solutions to the Assembly Line Balancing Problem 6.4 Community Detection Algorithms 6.5 Introduction to Hypergraph-Based Analytics References 7 Analytic Hierarchy Process and Multilayer Network-Based Method for Assembly Line Balancing 7.1 Problem Formulation of Multilayer Based, Multi-objective ... 7.1.1 Multilayer Network-Based Representation of Production Lines 7.1.2 The Objective Function of Assembly Line Balancing 7.2 Simulated Annealing-Based Line-Balancing Optimization 7.3 Solving ALB with Multilayer and AHP Approach 7.4 Parameter Testing 7.5 Complex, Multilayer Analysis of a Wire-Harness Assembly Graph Network 7.6 Summary of the Proposed Assembly Line Balancing Method References 8 Network Community Detection Algorithm for Graph Networks 8.1 The State-of-the-Art Nature of the Problem 8.1.1 Cost Function—Modularity 8.1.2 Overview of Recent Research Results in the Field of Community Detection Algorithms 8.1.3 Crossing Minimization-Based Serialization Method 8.1.4 Bottom-Up Segmentation-Based Community Detection Method 8.2 Proposed Methodology—Crossing Minimization and Bottom-Up ... 8.2.1 The Proposed Crossing Minimization-Based Serialization 8.2.2 The Proposed Bottom-Up Segmentation-Based Community Detection 8.2.3 Complexity Analysis 8.3 Results and Discussion of the Developed Combined Algorithm 8.3.1 Details of the Applied Metrics and Other Algorithms to Compare 8.3.2 Tuning of the Resolution and Gamma Parameters of the Algorithm 8.3.3 Comparing the Performance of the Algorithm with Other Methods 8.3.4 Benchmark Tests on LFR Artificial Networks 8.4 Summary of the Proposed Network Community Detection Method References 9 Hypergraph-Based Analysis of Collaborative Manufacturing 9.1 Collaborative Manufacturing 9.2 Higher-Order Network Representation to Support Collaboration 9.2.1 Hypergraphs for Modeling Complex Manufacturing Systems 9.2.2 Basics of Hypergraph Analytics 9.2.3 Hypergraph-Based Modeling of a Production Process 9.2.4 Advanced Hypergraph-Based Analysis of a Collaborative Manufacturing 9.3 Designing Collaborative Manufacturing for a Wire Harness Assembly Process 9.3.1 Hypergraph-Based Representation of Collaborative Manufacturing Designed for the Wire Harness Assembly Line 9.3.2 Identification of the Critical Elements and Collaborations 9.3.3 Segmentation of the Collaborative Manufacturing Model 9.3.4 Discussion on the Benefits of the Hypergraph-Based Analysis and Suggestions for Future Research 9.4 Summary of Hypergraph-Based Analysis of Collaborative Manufacturing References 10 Source List for Semantic-Based Modeling, Utilization of Graph Databases and Graph-Based Optimization of Manufacturing Systems 10.1 Ontology Development Methodology 10.2 Applied Methodologies and Software Tools for a Specific Knowledge Graph 10.3 List of Software Tools References 11 Conclusions Appendix Appendix A.1 Wire Harness Assembly Based Industrial Case Study—General A.2 Wire Harness Assembly Based Industrial Case Study—Collaboration A.3 Detailed Structural Diagram of the Case Study Specific KG A.4 Assembly Line Balancing Algorithm—Nominations A.5 Community Detection—List of the used Nomenclature and Benchmark Results References