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.