Kooperativer Bibliotheksverbund

UID:

Umfang: 1 Online-Ressource

ISBN: 9783031444975

Serie: Interdisciplinary excellence accelerator series

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-44496-8

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-44499-9

Sprache: Englisch

DOI: 10.1007/978-3-031-44497-5

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

Mehr zum Autor: Piller, Frank T. 1969-

Mehr zum Autor: Brecher, Christian 1969-

Mehr zum Autor: Aalst, Wil van der 1966-

Mehr zum Autor: Schuh, Günther 1958-

Mehr zum Autor: Jarke, Matthias 1952-2024

UID:

Umfang: 1 Online-Ressource (521 p.)

ISBN: 9783031444975 , 9783031444968

Serie: Interdisciplinary Excellence Accelerator Series

Inhalt: This seminal compendium, available through open access, illuminates the forefront of digital collaboration in production. It introduces the visionary concept of the Internet of Production (IoP), an ambitious initiative by Germany's esteemed Cluster of Excellence at RWTH Aachen University. This handbook pioneers the integration of data, models, and knowledge across development, production, and user cycles, offering interdisciplinary insights into production technology's horizons with the overall objective to create a worldwide lab. The work is organized into seven key parts, each contributing to a comprehensive understanding of the IoP. Part I lays the foundation with interdisciplinary visions and concepts. Part II delves into IoP's infrastructure, encompassing digital shadows and actionable artificial intelligence. Part III examines materials within the digitalized production landscape. Part IV confronts the challenges and potentials of production processes under novel digitalization methods. Part V focuses on production management with data-driven decision support, while Part VI explores agile development processes. Finally, Part VII delves into the interplay between internal and external perspectives in the IoP, human-centered work design, and platform-based ecosystems. Supported by the German Research Foundation (DFG), this compendium redefines manufacturing through the transformative IoP lens. Embrace this scholarly endeavor to embrace technological advancement. This is an open access book

Anmerkung: English

Sprache: Unbestimmte Sprache

Schlagwort(e): Electronic books.

URL: kostenfrei

URL: Click here to view book

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UID:

Umfang: 1 online resource (537 pages)

Ausgabe: 1st ed. 2024.

ISBN: 3-031-44497-3

Serie: Interdisciplinary Excellence Accelerator Series,

Inhalt: This seminal compendium, available through open access, illuminates the forefront of digital collaboration in production. It introduces the visionary concept of the Internet of Production (IoP), an ambitious initiative by Germany's esteemed Cluster of Excellence at RWTH Aachen. This handbook pioneers the integration of data, models, and knowledge across development, production, and user cycles, offering interdisciplinary insights into production technology's horizons with the overall objective to create a worldwide lab. The work is organized into seven key parts, each contributing to a comprehensive understanding of the IoP. Part I lays the foundation with interdisciplinary visions and concepts. Part II delves into IoP's infrastructure, encompassing digital shadows and actionable artificial intelligence. Part III examines materials within the digitalized production landscape. Part IV confronts the challenges and potentials of production processes under novel digitalization methods. Part V focuses on production management with data-driven decision support, while Part VI explores agile development processes. Finally, Part VII delves into the interplay between internal and external perspectives in the IoP, human-centered work design, and platform-based ecosystems. Supported by the German Research Foundation (DFG), this compendium redefines manufacturing through the transformative IoP lens. Embrace this scholarly endeavor to embrace technological advancement. This is an open access book.

Anmerkung: Introducing the Internet of Production -- Infrastructure -- Materials -- Production -- Production Management -- Agile Development -- Integrated Usage.

Weitere Ausg.: ISBN 3-031-44496-5

Sprache: Englisch

DOI: 10.1007/978-3-031-44497-5

UID:

Umfang: XXXV, 521 p. 160 illus., 140 illus. in color. , online resource.

Ausgabe: 1st ed. 2024.

ISBN: 9783031444975

Serie: Interdisciplinary Excellence Accelerator Series,

Inhalt: This seminal compendium, available through open access, illuminates the forefront of digital collaboration in production. It introduces the visionary concept of the Internet of Production (IoP), an ambitious initiative by Germany's esteemed Cluster of Excellence at RWTH Aachen. This handbook pioneers the integration of data, models, and knowledge across development, production, and user cycles, offering interdisciplinary insights into production technology's horizons with the overall objective to create a worldwide lab. The work is organized into seven key parts, each contributing to a comprehensive understanding of the IoP. Part I lays the foundation with interdisciplinary visions and concepts. Part II delves into IoP's infrastructure, encompassing digital shadows and actionable artificial intelligence. Part III examines materials within the digitalized production landscape. Part IV confronts the challenges and potentials of production processes under novel digitalization methods. Part V focuses on production management with data-driven decision support, while Part VI explores agile development processes. Finally, Part VII delves into the interplay between internal and external perspectives in the IoP, human-centered work design, and platform-based ecosystems. Supported by the German Research Foundation (DFG), this compendium redefines manufacturing through the transformative IoP lens. Embrace this scholarly endeavor to embrace technological advancement. This is an open access book.

Anmerkung: Introducing the Internet of Production -- Infrastructure -- Materials -- Production -- Production Management -- Agile Development -- Integrated Usage.

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9783031444968

Weitere Ausg.: Printed edition: ISBN 9783031444982

Weitere Ausg.: Printed edition: ISBN 9783031444999

Sprache: Englisch

DOI: 10.1007/978-3-031-44497-5

URL: https://doi.org/10.1007/978-3-031-44497-5

UID:

Umfang: 1 Online-Ressource (XXXV, 521 p. 160 illus., 140 illus. in color)

Ausgabe: 1st ed. 2024

ISBN: 9783031444975

Serie: Interdisciplinary Excellence Accelerator Series

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-44496-8

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-44498-2

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-44499-9

Weitere Ausg.: Erscheint auch als Online-Ausgabe ISBN 978-3-031-44497-5

Sprache: Englisch

Mehr zum Autor: Brecher, Christian 1969-

UID:

Umfang: 1 online resource (537 pages)

Ausgabe: 1st ed.

ISBN: 9783031444975

Serie: Interdisciplinary Excellence Accelerator Series

Anmerkung: 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.

Weitere Ausg.: Print version: Brecher, Christian Internet of Production Cham : Springer International Publishing AG,c2024 ISBN 9783031444968

Sprache: Englisch

Schlagwort(e): Electronic books.

URL: Click to View

UID:

Umfang: 1 online resource (537 pages)

Ausgabe: First edition.

ISBN: 3-031-44497-3

Serie: Interdisciplinary Excellence Accelerator Series.

Anmerkung: 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.

Weitere Ausg.: ISBN 3-031-44496-5

Sprache: Englisch

UID:

Umfang: 1 online resource (537 pages)

Ausgabe: First edition.

ISBN: 3-031-44497-3

Serie: Interdisciplinary Excellence Accelerator Series.

Anmerkung: 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.

Weitere Ausg.: ISBN 3-031-44496-5

Sprache: Englisch

UID:

Umfang: 1 online resource : , illustrations

ISBN: 3-030-98062-6

Inhalt: The Internet of Production fosters collaboration between humans, machines, data and models in industrial production. This book provides research results from the Cluster of Excellence at RWTH Aachen University. Clusters of Excellence are the backbone of the German Excellence Strategy, providing longterm interdisciplinary research at University of Excellence. This is an open access book. This is an open access book.

Sprache: Englisch

UID:

Umfang: 1 online resource : , illustrations

ISBN: 3-030-98062-6

Inhalt: The Internet of Production fosters collaboration between humans, machines, data and models in industrial production. This book provides research results from the Cluster of Excellence at RWTH Aachen University. Clusters of Excellence are the backbone of the German Excellence Strategy, providing longterm interdisciplinary research at University of Excellence. This is an open access book. This is an open access book.

Sprache: Englisch