Internet of Production : : Fundamentals, Methods and Applications.

• Author: Brecher, Christian.
• TeilnehmendeR: Schuh, Günther.; van der Aalst, Wil.; Jarke, Matthias.; Piller, Frank T., 1969-; Padberg, Melanie.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2024.; ©2024.
• Year of Publication: 2024
• Edition: First edition.
• Language: English
• Series: Interdisciplinary Excellence Accelerator Series.
• Physical Description: 1 online resource (537 pages)

Table of Contents

• Intro
• Preface
• Crossing Disciplinary Boundaries: RWTH Aachen and Springer Start a New Publishing Partnership
• Tenet 1: Reduce the Time Between Research, Publication, and Scholarly Knowledge Transfer
• Tenet 2: Make Interdisciplinary Review Mandatory
• Tenet 3: Use books as calls to action and solution vehicles
• Editorial
• Contents
• About the Editors
• Section Editors
• Contributors
• Part I Introducing the Internet of Production
• 1 The Internet of Production: Interdisciplinary Visions and Concepts for the Production of Tomorrow
• Contents
• 1.1 Introduction
• 1.2 Research Domains in Production
• 1.3 Objectives of the Internet of Production
• 1.4 Fostering Interdisciplinary Research for the IoP
• 1.5 Conclusion
• References
• Part II IoP - Infrastructure
• 2 Digital Shadows: Infrastructuring the Internet of Production
• Contents
• 2.1 Introduction
• 2.2 Related Work on Digital Twins and Digital Shadows
• 2.3 Infrastructure Requirements and DS Perspectives
• 2.3.1 Functional Perspective: Data-to-Knowledge Pipelines Using Domain-Specific Digital Shadows
• 2.3.2 Conceptual Perspective: Organizing DS Collections in a WWL
• 2.3.3 Physical Perspective: Interconnected Technical Infrastructure
• 2.3.4 Toward an Empirically Grounded IoP Infrastructure
• 2.4 Example of a Successful DS-Based Metamodel: Process Mining
• 2.5 Conclusion
• References
• 3 Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead
• Contents
• 3.1 Introduction
• 3.2 State of the Art: Challenges for the Infrastructure
• 3.2.1 An Overview of the Infrastructure of Production
• 3.2.2 Research Areas for the Infrastructure of Production
• 3.2.2.1 Scalable Processing of Data in Motion and at Rest
• 3.2.2.2 Device Interoperability
• 3.2.2.3 Data Security and Data Quality
• 3.2.2.4 Network Security.
• 3.2.2.5 Infrastructure for Secure Industrial Collaboration
• 3.3 Evolving Today's Infrastructure for Future Industry Use
• 3.3.1 Scalable Processing of Data in Motion and at Rest
• 3.3.2 Device Interoperability
• 3.3.3 Data Security and Data Quality
• 3.3.4 Network Security
• 3.3.5 Infrastructure for Secure Industrial Collaboration
• 3.4 Conclusion
• References
• 4 A Digital Shadow Reference Model for WorldwideProduction Labs
• Contents
• 4.1 Introduction
• 4.2 State of the Art
• 4.3 The Digital Shadow Reference Model
• 4.4 Ontologies in the Internet of Production
• 4.5 Data, Models, and Semantics in Selected Use Cases
• 4.5.1 Production Planning in Injection Molding
• 4.5.2 Process Control in Injection Molding
• 4.5.3 Adaptable Layerwise Laser-Based Manufacturing
• 4.5.4 Automated Factory Planning
• 4.6 A Method to Design Digital Shadows
• 4.7 Data and Model Life Cycles in the IoP
• 4.8 Outlook: Using Digital Shadows in Digital Twins
• 4.9 Conclusion
• References
• 5 Actionable Artificial Intelligence for the Future of Production
• Contents
• 5.1 Introduction
• 5.2 Autonomous Agents Beyond Company Boundaries
• 5.3 Machine Level
• 5.3.1 Data-Driven Quality Assurance and Process Control of Laser Powder Bed Fusion
• 5.3.2 Data-Driven Robot Laser Material Processing
• 5.3.3 Structured Learning for Robot Control
• 5.3.4 Reactive Modular Task-Level Control for Industrial Robotics
• 5.3.5 Increasing Confidence in the Correctness of Reconfigurable Control Software
• 5.4 Process Level
• 5.4.1 Mining Shop Floor-Level Processes
• 5.4.2 Challenges in the Textile Industry
• 5.4.3 Analyzing Process Dynamics
• 5.5 Overarching Principles
• 5.5.1 Generative Models for Production
• 5.5.2 Concept Extraction for Industrial Classification
• 5.5.3 Inverse Problems via Filtering Methods.
• 5.5.4 Immersive Visualization of Artificial Neural Networks
• 5.5.5 IoP-Wide Process Data Capture and Management
• 5.6 Conclusion
• References
• Part III Materials
• 6 Materials Within a Digitalized Production Environment
• Contents
• 6.1 Introduction
• 6.2 ICME in a Production Environment
• 6.3 Integrated Structural Health Engineering
• 6.4 Machine Learning
• 6.5 Ontologies for ICME
• 6.5.1 Ontologies in Materials
• 6.5.2 Ontologies in Production
• 6.5.3 Modular Configurable and Re-Usable Ontologies
• 6.6 Simulation Platforms
• 6.7 Conclusion
• References
• 7 Material Solutions to Increase the Information Density in Mold-Based Production Systems
• Contents
• 7.1 Introduction
• 7.2 Powder and Alloy Development for Additive Manufacturing
• 7.3 Smart Coatings
• 7.4 Laser Ablation
• 7.5 Molecular Dynamics for Digital Representation of Polymers
• References
• 8 Toward Holistic Digital Material Description During Press-Hardening
• Contents
• 8.1 Introduction
• 8.2 Digital Description of Material for Press-Hardening
• 8.3 Digitalization of Material Behavior During Deformation
• 8.4 Digitalized Press-Hardening Tool
• 8.5 Data-Driven Material Description of Press-Hardening Tools
• 8.6 Conclusions
• References
• 9 Materials in the Drive Chain - Modeling Materials for the Internet of Production
• Contents
• 9.1 Introduction
• 9.1.1 Fine Blanking
• 9.1.2 High-Strength Sintered Gear
• 9.1.3 Drive Shaft
• 9.2 Fine Blanking - Artificial Intelligence (AI) for Sheet Metal Hardness Classification
• 9.3 Sintered Gear - Simulation of Sintering
• 9.4 Sintered Gear - Surface Hardening and Load-Bearing Capacity
• 9.5 Sintered Gear - Grinding and Surface Integrity
• 9.6 Drive Shaft - Open-Die Forging
• 9.7 Drive Shaft - Machinability
• 9.8 Summary
• References
• Part IV Production.
• 10 Internet of Production: Challenges, Potentials, and Benefits for Production Processes due to Novel Methods in Digitalization
• Contents
• 10.1 Introduction
• 10.2 Challenges for Industrial Manufacturing
• 10.3 Potential and Benefits
• 10.4 The Approach of the "Internet of Production"
• 10.5 Conclusion
• References
• 11 Model-Based Controlling Approaches for ManufacturingProcesses
• Contents
• 11.1 Introduction
• 11.2 State of the Art
• 11.3 Domain Application
• 11.3.1 Data Aggregation and Sensors
• 11.3.2 Data-Based Model Identification and Optimization
• 11.3.3 Autonomous Systems and Decision Support
• 11.3.4 Model and Data Integration in Connected Job Shops
• 11.4 Conclusion and Outlook
• References
• 12 Improving Manufacturing Efficiency for Discontinuous Processes by Methodological Cross-Domain Knowledge Transfer
• Contents
• 12.1 Introduction
• 12.2 Common Challenges in Modeling and Optimization of Discontinuous Processes
• 12.3 High Granularity Process Data Collection and Assessments to Recognize Second- and Third-Order Process Interdependencies in a HPDC Process
• 12.4 Fourier Ptychography-Based Imaging System for Far-Field Microscope
• 12.5 Integrating Reduced Models and ML to Meta-Modeling Laser Manufacturing Processes
• 12.6 Vision-Based Error Detection in Automated Tape Placement for Model-Based Process Optimization
• 12.7 Understanding Coating Processes Based on ML-Models
• 12.8 Transfer Learning in Injection Molding for Process Model Training
• 12.9 Assistance System for Open-Die Forging Using Fast Models
• 12.10 Development of a Predictive Model for the Burr Formation During Laser Fusion Cutting of Metals
• 12.11 Individualized Production by the Use of Microservices: A Holistic Approach
• 12.12 Conclusion
• References.
• 13 Decision Support for the Optimization of Continuous Processes using Digital Shadows
• Contents
• 13.1 Introduction
• 13.2 Single Process for Plastics: Profile Extrusion
• 13.2.1 Prerequisites for Digital Shadows
• 13.2.2 Shape Optimization with Reinforcement Learning
• 13.3 Metal Processing Process Chain: Rolling, Tempering, and Fine Blanking
• 13.3.1 Prerequisites for Digital Shadows
• 13.3.1.1 (Hot) Rolling + Tempering
• 13.3.1.2 Data Analysis of the Fine Blanking Process
• 13.3.2 Process Design and Optimization with Reinforcement Learning
• 13.4 Conclusion and Outlook
• References
• 14 Modular Control and Services to Operate Lineless Mobile Assembly Systems
• Contents
• 14.1 The Future of Assembly
• 14.2 Modular Levels and Layers for LMAS Operation
• 14.3 Toward Modular Station-Level Control Through Formation Planning ofMobile Robots
• 14.3.1 Tool-Dependent Reachability Measure
• 14.3.2 Outlook
• 14.4 Consensus and Coordination in Sensor-Robot Network
• 14.4.1 System Modeling
• 14.4.2 Motion Planning Algorithms
• 14.5 Leveraging Distributed Computing Resources in the Network
• 14.5.1 Laying the Groundwork for In-Network Control
• 14.5.2 Toward Deployable In-Network Control
• 14.6 Trustworthy Vision Solutions Through Interpretable AI
• 14.6.1 Interpretable Machine-Learned Features Using Generative Deep Learning
• 14.6.2 Initial Implementation on a Synthetic Dataset
• 14.7 Multipurpose Input Device for Human-Robot Collaboration
• 14.7.1 Application, Implementation, and Result
• 14.7.2 Outlook
• 14.8 Ontology-Based Knowledge Management in Process Configuration
• 14.8.1 Concept and Implementation
• 14.8.2 Summary and Outlook
• 14.9 Conclusion
• References
• Part V Production Management
• 15 Methods and Limits of Data-Based Decision Support in Production Management
• Contents
• 15.1 Introduction.
• 15.2 Increasing Decision and Implementation Speed in Short-Term Production Management.