Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes.
Saved in:
: | |
---|---|
TeilnehmendeR: | |
Place / Publishing House: | Cham : : Springer International Publishing AG,, 2023. ©2023. |
Year of Publication: | 2023 |
Edition: | 1st ed. |
Language: | English |
Online Access: | |
Physical Description: | 1 online resource (305 pages) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
50030625775 |
---|---|
ctrlnum |
(MiAaPQ)50030625775 (Au-PeEL)EBL30625775 (OCoLC)1390758028 |
collection |
bib_alma |
record_format |
marc |
spelling |
Aurich, Jan C. Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. 1st ed. Cham : Springer International Publishing AG, 2023. ©2023. 1 online resource (305 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Intro -- Preface -- Acknowledgement -- Contents -- List of Contributors -- Discrete Filter and Non-Gaussian Noise for Fast Roughness Simulations with Gaussian Processes -- 1 Introduction -- 2 Background -- 2.1 Roughness Model with Gaussian Processes -- 2.2 Simulation of Rough Surfaces -- 2.3 Related Work -- 3 Gaussian Process Filter -- 3.1 Discrete Filter -- 3.2 Discrete Filter with FFT -- 4 Experiments -- 4.1 Timings of the Discrete Filter with SciPy and CuFFT -- 4.2 Benchmarking Discrete Filter -- 5 Applications -- 6 Conclusion -- References -- Phase Field Simulations for Fatigue Failure Prediction in Manufacturing Processes -- 1 Introduction -- 2 A Phase Field Model for Cyclic Fatigue -- 2.1 A Time-Cycle Transformation in the Phase Field Fatigue Model -- 3 Phase Field Model in the Context of Manufacturing Process -- 3.1 Application in the Cold Forging Process -- 3.2 Modeling Cold Forging Process Using Phase Field Method -- 3.3 Phase Field Fatigue Model in Cylindrical Coordinate System -- 3.4 Phase Field Simulation of Cold Forging Process -- 4 Conclusion -- References -- Embedding-Space Explanations of Learned Mixture Behavior -- 1 Introduction -- 2 Rangesets -- 2.1 Motivation -- 2.2 Rangeset Construction -- 2.3 Application to Process-Level -- 3 Decision Boundary Visualization -- 3.1 CoFFi -- 3.2 Chemical Classes in Latent Feature Space -- 3.3 Latent Features in Physicochemical Descriptor Space -- 4 Conclusion and Future Work -- References -- Insight into Indentation Processes of Ni-Graphene Nanocomposites by Molecular Dynamics Simulation -- 1 Introduction -- 2 Method -- 3 Ni Single Crystal -- 4 Ni Bi-crystal -- 5 Ni Polycrystal -- 6 Summary -- References -- Physical Modeling of Grinding Forces -- 1 Introduction -- 2 Experimental Investigation -- 2.1 Requirements for Performing Experiments -- 2.2 Preparations for the Scratch Tests. 2.3 Performing Scratch Tests in Dry Conditions -- 2.4 Performing Scratch Tests in Wet Conditions -- 3 Development of the Grinding Model -- 3.1 Selection of the Suitable Material Model -- 3.2 Discretization Approaches -- 3.3 Simulative Integration of the Cooling Lubricants -- 4 Conclusion -- References -- Modeling and Implementation of a 5G-Enabled Digital Twin of a Machine Tool Based on Physics Simulation -- 1 Motivation -- 2 State of the Art -- 2.1 5G Communication Standard -- 2.2 Physics Simulation in Manufacturing -- 2.3 Digital Twin in Manufacturing -- 3 Modeling of the Architecture for 5G-Enabled Digital Twin -- 3.1 Objectives and Requirements -- 3.2 System Architecture -- 3.3 Interactions and Information Flow -- 4 Implementation -- 4.1 Real System -- 4.2 Communication System -- 4.3 Digital System -- 4.4 Benefits and Challenges -- 5 Summary and Outlook -- References -- A Human-Centered Framework for Scalable Extended Reality Spaces -- 1 Introduction -- 2 Background -- 2.1 Terminology -- 2.2 Developing Collaborative Extended Reality Applications -- 3 XRS Framework: Basic Concept -- 3.1 Scalable Extended Reality (XRS) -- 3.2 Context of Use -- 4 XRS Framework: Requirements -- 4.1 Functional Requirements -- 4.2 Non-functional Requirements -- 5 XRS Framework: Design Solution -- 5.1 Access Points and Data - RQs 1, 2, 17, 18 -- 5.2 Subscribing to Collaborators - RQs 11, 12, 19 -- 5.3 Visualizing Static Scene Components - RQ 13 -- 5.4 Visualizing Dynamic Scene Components - RQs 14, 15, 16 -- 5.5 Visualizing User Location and Activity - RQs 11, 12 -- 5.6 Referencing Scene Components - RQs 3, 4, 7, 8 -- 5.7 Manipulating Dynamic Scene Components - RQs 5, 6, 9, 10 -- 5.8 Scalable Interaction Techniques - RQs 17, 18, 20 -- 6 XRS Framework: Walkthrough -- 6.1 Collaborative Prototyping -- 6.2 Training and Teleoperation -- 7 Conclusion -- References. A Holistic Framework for Factory Planning Using Reinforcement Learning -- 1 Introduction -- 2 State of the Art -- 2.1 Introduction to Factory Layout Planning -- 2.2 Approaches for the Early Phase of Factory Layout Planning -- 2.3 Introduction to Reinforcement Learning -- 3 Research Gap -- 4 Framework for Factory Layout Planning Using Reinforcement Learning -- 4.1 Requirements -- 4.2 Description of the Framework -- 5 Step 5: Manual Planning -- 5.1 Evaluation of the Framework -- 6 Conclusion and Outlook -- References -- Simulation-Based Investigation of the Distortion of Milled Thin-Walled Aluminum Structural Parts Due to Residual Stresses -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 3.1 Initial Bulk Residual Stress Characterization -- 3.2 Machining Induced Residual Stress Characterization -- 3.3 Machining Induced Residual Stress as Driver for Distortion -- 3.4 Superposition of IBRS and MIRS and Its Effect on Distortion -- 4 Simulation Models -- 4.1 Distortion Prediction Model -- 4.2 Cutting Model to Predict the MIRS -- 5 Development of Compensation Techniques -- 6 Summary -- References -- Prediction of Thermodynamic Properties of Fluids at Extreme Conditions: Assessment of the Consistency of Molecular-Based Models -- 1 Introduction -- 2 Methods -- 2.1 Brown's Characteristic Curves -- 2.2 Substances -- 2.3 Molecular Simulation -- 2.4 Molecular-Based Equation of States -- 3 Results -- 3.1 Lennard-Jones Fluids -- 3.2 Mie Fluids -- 3.3 Toluene, Ethanol, and Dimethyl Ether -- 4 Conclusions -- References -- A Methodology for Developing a Model for Energy Prediction in Additive Manufacturing Exemplified by High-Speed Laser Directed Energy Deposition -- 1 Introduction -- 2 State of the Art -- 2.1 High-Speed Laser Directed Energy Deposition as an Additive Manufacturing Process -- 2.2 Current Discussion of the Environmental Impact of DED. 2.3 Requirements -- 3 Approach for Creating an Energy Prediction Model -- 3.1 Capturing the Structure -- 3.2 Process Analysis -- 3.3 Analysis of the Process Parameters -- 3.4 Creating the Model -- 4 Example of an Application Using HS DED-LB -- 4.1 Capturing the Structure -- 4.2 Process Analysis -- 4.3 Analysis of the Process Parameters -- 4.4 Creating the Model -- 4.5 Exemplary Application and Validation -- 5 Conclusion -- References -- Framework to Improve the Energy Performance During Design for Additive Manufacturing -- 1 Introduction -- 2 Research Background -- 2.1 Energy Performance Issues in Additive Manufacturing -- 2.2 Research Target and Tasks for This Work -- 3 Framework of Energy Performance Improvement in DfAM -- 3.1 Overview of the Framework -- 3.2 Structural Topology Optimization -- 3.3 Tool-Path Length Assessment -- 3.4 Multi-player Competition Algorithm -- 4 Use Cases -- 4.1 Use Case 1: 2D Optimization Problem -- 4.2 Use Case 2: 3D Optimization Problem -- 5 Discussion -- 6 Conclusion and Outlook -- References -- Investigation of Micro Grinding via Kinematic Simulations -- 1 Introduction -- 2 Properties of the MPGTs -- 3 Model of the MPGT for Kinematic Simulations -- 3.1 Analysis of the Grit Size Distribution -- 3.2 Analysis of the Grit Shape -- 3.3 Requirements and Assumptions for the Tool Model -- 3.4 Modeling of the Virtual Bond of the Tool Model -- 3.5 Validation of the Bond Thickness -- 3.6 Modeling of the Abrasive Grits -- 3.7 Positioning of the Virtual Grit Representations on the Virtual Tool -- 3.8 Evaluation of the Grit Size on the Real Tool -- 3.9 Adaption of the Grit Sizes for the Tool Model -- 3.10 Conclusion on Tool Modeling -- 4 Setup of the Simulation -- 4.1 Workpiece Representation Within the Simulation -- 4.2 Kinematics and Time Discretization -- 4.3 Calculation of the Tool-Workpiece Intersection. 5 Application of the Simulation Model to the Investigation of Micro Grinding -- 5.1 Influence of the Feed Rate on the Resulting Surface Topography -- 5.2 Calculation of the Undeformed Chip Thickness -- 6 Conclusion and Outlook -- References -- Molecular Dynamics Simulation of Cutting Processes: The Influence of Cutting Fluids at the Atomistic Scale -- 1 Introduction -- 2 Methods -- 2.1 Simulation Scenario -- 2.2 Molecular Model -- 2.3 Definition of Observables -- 3 Results -- 3.1 Mechanical Properties -- 3.2 Workpiece Deformation -- 3.3 Lubrication and Formation of Tribofilm -- 3.4 Thermal Properties -- 3.5 Reproducibility -- 4 Conclusions -- References -- Visual Analysis and Anomaly Detection of Material Flow in Manufacturing -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Visualization -- 3 Discussion -- 4 Conclusion -- References -- Author Index. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Garth, Christoph. Linke, Barbara S. Print version: Aurich, Jan C. Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes Cham : Springer International Publishing AG,c2023 9783031357787 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30625775 Click to View |
language |
English |
format |
eBook |
author |
Aurich, Jan C. |
spellingShingle |
Aurich, Jan C. Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. Intro -- Preface -- Acknowledgement -- Contents -- List of Contributors -- Discrete Filter and Non-Gaussian Noise for Fast Roughness Simulations with Gaussian Processes -- 1 Introduction -- 2 Background -- 2.1 Roughness Model with Gaussian Processes -- 2.2 Simulation of Rough Surfaces -- 2.3 Related Work -- 3 Gaussian Process Filter -- 3.1 Discrete Filter -- 3.2 Discrete Filter with FFT -- 4 Experiments -- 4.1 Timings of the Discrete Filter with SciPy and CuFFT -- 4.2 Benchmarking Discrete Filter -- 5 Applications -- 6 Conclusion -- References -- Phase Field Simulations for Fatigue Failure Prediction in Manufacturing Processes -- 1 Introduction -- 2 A Phase Field Model for Cyclic Fatigue -- 2.1 A Time-Cycle Transformation in the Phase Field Fatigue Model -- 3 Phase Field Model in the Context of Manufacturing Process -- 3.1 Application in the Cold Forging Process -- 3.2 Modeling Cold Forging Process Using Phase Field Method -- 3.3 Phase Field Fatigue Model in Cylindrical Coordinate System -- 3.4 Phase Field Simulation of Cold Forging Process -- 4 Conclusion -- References -- Embedding-Space Explanations of Learned Mixture Behavior -- 1 Introduction -- 2 Rangesets -- 2.1 Motivation -- 2.2 Rangeset Construction -- 2.3 Application to Process-Level -- 3 Decision Boundary Visualization -- 3.1 CoFFi -- 3.2 Chemical Classes in Latent Feature Space -- 3.3 Latent Features in Physicochemical Descriptor Space -- 4 Conclusion and Future Work -- References -- Insight into Indentation Processes of Ni-Graphene Nanocomposites by Molecular Dynamics Simulation -- 1 Introduction -- 2 Method -- 3 Ni Single Crystal -- 4 Ni Bi-crystal -- 5 Ni Polycrystal -- 6 Summary -- References -- Physical Modeling of Grinding Forces -- 1 Introduction -- 2 Experimental Investigation -- 2.1 Requirements for Performing Experiments -- 2.2 Preparations for the Scratch Tests. 2.3 Performing Scratch Tests in Dry Conditions -- 2.4 Performing Scratch Tests in Wet Conditions -- 3 Development of the Grinding Model -- 3.1 Selection of the Suitable Material Model -- 3.2 Discretization Approaches -- 3.3 Simulative Integration of the Cooling Lubricants -- 4 Conclusion -- References -- Modeling and Implementation of a 5G-Enabled Digital Twin of a Machine Tool Based on Physics Simulation -- 1 Motivation -- 2 State of the Art -- 2.1 5G Communication Standard -- 2.2 Physics Simulation in Manufacturing -- 2.3 Digital Twin in Manufacturing -- 3 Modeling of the Architecture for 5G-Enabled Digital Twin -- 3.1 Objectives and Requirements -- 3.2 System Architecture -- 3.3 Interactions and Information Flow -- 4 Implementation -- 4.1 Real System -- 4.2 Communication System -- 4.3 Digital System -- 4.4 Benefits and Challenges -- 5 Summary and Outlook -- References -- A Human-Centered Framework for Scalable Extended Reality Spaces -- 1 Introduction -- 2 Background -- 2.1 Terminology -- 2.2 Developing Collaborative Extended Reality Applications -- 3 XRS Framework: Basic Concept -- 3.1 Scalable Extended Reality (XRS) -- 3.2 Context of Use -- 4 XRS Framework: Requirements -- 4.1 Functional Requirements -- 4.2 Non-functional Requirements -- 5 XRS Framework: Design Solution -- 5.1 Access Points and Data - RQs 1, 2, 17, 18 -- 5.2 Subscribing to Collaborators - RQs 11, 12, 19 -- 5.3 Visualizing Static Scene Components - RQ 13 -- 5.4 Visualizing Dynamic Scene Components - RQs 14, 15, 16 -- 5.5 Visualizing User Location and Activity - RQs 11, 12 -- 5.6 Referencing Scene Components - RQs 3, 4, 7, 8 -- 5.7 Manipulating Dynamic Scene Components - RQs 5, 6, 9, 10 -- 5.8 Scalable Interaction Techniques - RQs 17, 18, 20 -- 6 XRS Framework: Walkthrough -- 6.1 Collaborative Prototyping -- 6.2 Training and Teleoperation -- 7 Conclusion -- References. A Holistic Framework for Factory Planning Using Reinforcement Learning -- 1 Introduction -- 2 State of the Art -- 2.1 Introduction to Factory Layout Planning -- 2.2 Approaches for the Early Phase of Factory Layout Planning -- 2.3 Introduction to Reinforcement Learning -- 3 Research Gap -- 4 Framework for Factory Layout Planning Using Reinforcement Learning -- 4.1 Requirements -- 4.2 Description of the Framework -- 5 Step 5: Manual Planning -- 5.1 Evaluation of the Framework -- 6 Conclusion and Outlook -- References -- Simulation-Based Investigation of the Distortion of Milled Thin-Walled Aluminum Structural Parts Due to Residual Stresses -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 3.1 Initial Bulk Residual Stress Characterization -- 3.2 Machining Induced Residual Stress Characterization -- 3.3 Machining Induced Residual Stress as Driver for Distortion -- 3.4 Superposition of IBRS and MIRS and Its Effect on Distortion -- 4 Simulation Models -- 4.1 Distortion Prediction Model -- 4.2 Cutting Model to Predict the MIRS -- 5 Development of Compensation Techniques -- 6 Summary -- References -- Prediction of Thermodynamic Properties of Fluids at Extreme Conditions: Assessment of the Consistency of Molecular-Based Models -- 1 Introduction -- 2 Methods -- 2.1 Brown's Characteristic Curves -- 2.2 Substances -- 2.3 Molecular Simulation -- 2.4 Molecular-Based Equation of States -- 3 Results -- 3.1 Lennard-Jones Fluids -- 3.2 Mie Fluids -- 3.3 Toluene, Ethanol, and Dimethyl Ether -- 4 Conclusions -- References -- A Methodology for Developing a Model for Energy Prediction in Additive Manufacturing Exemplified by High-Speed Laser Directed Energy Deposition -- 1 Introduction -- 2 State of the Art -- 2.1 High-Speed Laser Directed Energy Deposition as an Additive Manufacturing Process -- 2.2 Current Discussion of the Environmental Impact of DED. 2.3 Requirements -- 3 Approach for Creating an Energy Prediction Model -- 3.1 Capturing the Structure -- 3.2 Process Analysis -- 3.3 Analysis of the Process Parameters -- 3.4 Creating the Model -- 4 Example of an Application Using HS DED-LB -- 4.1 Capturing the Structure -- 4.2 Process Analysis -- 4.3 Analysis of the Process Parameters -- 4.4 Creating the Model -- 4.5 Exemplary Application and Validation -- 5 Conclusion -- References -- Framework to Improve the Energy Performance During Design for Additive Manufacturing -- 1 Introduction -- 2 Research Background -- 2.1 Energy Performance Issues in Additive Manufacturing -- 2.2 Research Target and Tasks for This Work -- 3 Framework of Energy Performance Improvement in DfAM -- 3.1 Overview of the Framework -- 3.2 Structural Topology Optimization -- 3.3 Tool-Path Length Assessment -- 3.4 Multi-player Competition Algorithm -- 4 Use Cases -- 4.1 Use Case 1: 2D Optimization Problem -- 4.2 Use Case 2: 3D Optimization Problem -- 5 Discussion -- 6 Conclusion and Outlook -- References -- Investigation of Micro Grinding via Kinematic Simulations -- 1 Introduction -- 2 Properties of the MPGTs -- 3 Model of the MPGT for Kinematic Simulations -- 3.1 Analysis of the Grit Size Distribution -- 3.2 Analysis of the Grit Shape -- 3.3 Requirements and Assumptions for the Tool Model -- 3.4 Modeling of the Virtual Bond of the Tool Model -- 3.5 Validation of the Bond Thickness -- 3.6 Modeling of the Abrasive Grits -- 3.7 Positioning of the Virtual Grit Representations on the Virtual Tool -- 3.8 Evaluation of the Grit Size on the Real Tool -- 3.9 Adaption of the Grit Sizes for the Tool Model -- 3.10 Conclusion on Tool Modeling -- 4 Setup of the Simulation -- 4.1 Workpiece Representation Within the Simulation -- 4.2 Kinematics and Time Discretization -- 4.3 Calculation of the Tool-Workpiece Intersection. 5 Application of the Simulation Model to the Investigation of Micro Grinding -- 5.1 Influence of the Feed Rate on the Resulting Surface Topography -- 5.2 Calculation of the Undeformed Chip Thickness -- 6 Conclusion and Outlook -- References -- Molecular Dynamics Simulation of Cutting Processes: The Influence of Cutting Fluids at the Atomistic Scale -- 1 Introduction -- 2 Methods -- 2.1 Simulation Scenario -- 2.2 Molecular Model -- 2.3 Definition of Observables -- 3 Results -- 3.1 Mechanical Properties -- 3.2 Workpiece Deformation -- 3.3 Lubrication and Formation of Tribofilm -- 3.4 Thermal Properties -- 3.5 Reproducibility -- 4 Conclusions -- References -- Visual Analysis and Anomaly Detection of Material Flow in Manufacturing -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Visualization -- 3 Discussion -- 4 Conclusion -- References -- Author Index. |
author_facet |
Aurich, Jan C. Garth, Christoph. Linke, Barbara S. |
author_variant |
j c a jc jca |
author2 |
Garth, Christoph. Linke, Barbara S. |
author2_variant |
c g cg b s l bs bsl |
author2_role |
TeilnehmendeR TeilnehmendeR |
author_sort |
Aurich, Jan C. |
title |
Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. |
title_full |
Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. |
title_fullStr |
Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. |
title_full_unstemmed |
Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. |
title_auth |
Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. |
title_new |
Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. |
title_sort |
proceedings of the 3rd conference on physical modeling for virtual manufacturing systems and processes. |
publisher |
Springer International Publishing AG, |
publishDate |
2023 |
physical |
1 online resource (305 pages) |
edition |
1st ed. |
contents |
Intro -- Preface -- Acknowledgement -- Contents -- List of Contributors -- Discrete Filter and Non-Gaussian Noise for Fast Roughness Simulations with Gaussian Processes -- 1 Introduction -- 2 Background -- 2.1 Roughness Model with Gaussian Processes -- 2.2 Simulation of Rough Surfaces -- 2.3 Related Work -- 3 Gaussian Process Filter -- 3.1 Discrete Filter -- 3.2 Discrete Filter with FFT -- 4 Experiments -- 4.1 Timings of the Discrete Filter with SciPy and CuFFT -- 4.2 Benchmarking Discrete Filter -- 5 Applications -- 6 Conclusion -- References -- Phase Field Simulations for Fatigue Failure Prediction in Manufacturing Processes -- 1 Introduction -- 2 A Phase Field Model for Cyclic Fatigue -- 2.1 A Time-Cycle Transformation in the Phase Field Fatigue Model -- 3 Phase Field Model in the Context of Manufacturing Process -- 3.1 Application in the Cold Forging Process -- 3.2 Modeling Cold Forging Process Using Phase Field Method -- 3.3 Phase Field Fatigue Model in Cylindrical Coordinate System -- 3.4 Phase Field Simulation of Cold Forging Process -- 4 Conclusion -- References -- Embedding-Space Explanations of Learned Mixture Behavior -- 1 Introduction -- 2 Rangesets -- 2.1 Motivation -- 2.2 Rangeset Construction -- 2.3 Application to Process-Level -- 3 Decision Boundary Visualization -- 3.1 CoFFi -- 3.2 Chemical Classes in Latent Feature Space -- 3.3 Latent Features in Physicochemical Descriptor Space -- 4 Conclusion and Future Work -- References -- Insight into Indentation Processes of Ni-Graphene Nanocomposites by Molecular Dynamics Simulation -- 1 Introduction -- 2 Method -- 3 Ni Single Crystal -- 4 Ni Bi-crystal -- 5 Ni Polycrystal -- 6 Summary -- References -- Physical Modeling of Grinding Forces -- 1 Introduction -- 2 Experimental Investigation -- 2.1 Requirements for Performing Experiments -- 2.2 Preparations for the Scratch Tests. 2.3 Performing Scratch Tests in Dry Conditions -- 2.4 Performing Scratch Tests in Wet Conditions -- 3 Development of the Grinding Model -- 3.1 Selection of the Suitable Material Model -- 3.2 Discretization Approaches -- 3.3 Simulative Integration of the Cooling Lubricants -- 4 Conclusion -- References -- Modeling and Implementation of a 5G-Enabled Digital Twin of a Machine Tool Based on Physics Simulation -- 1 Motivation -- 2 State of the Art -- 2.1 5G Communication Standard -- 2.2 Physics Simulation in Manufacturing -- 2.3 Digital Twin in Manufacturing -- 3 Modeling of the Architecture for 5G-Enabled Digital Twin -- 3.1 Objectives and Requirements -- 3.2 System Architecture -- 3.3 Interactions and Information Flow -- 4 Implementation -- 4.1 Real System -- 4.2 Communication System -- 4.3 Digital System -- 4.4 Benefits and Challenges -- 5 Summary and Outlook -- References -- A Human-Centered Framework for Scalable Extended Reality Spaces -- 1 Introduction -- 2 Background -- 2.1 Terminology -- 2.2 Developing Collaborative Extended Reality Applications -- 3 XRS Framework: Basic Concept -- 3.1 Scalable Extended Reality (XRS) -- 3.2 Context of Use -- 4 XRS Framework: Requirements -- 4.1 Functional Requirements -- 4.2 Non-functional Requirements -- 5 XRS Framework: Design Solution -- 5.1 Access Points and Data - RQs 1, 2, 17, 18 -- 5.2 Subscribing to Collaborators - RQs 11, 12, 19 -- 5.3 Visualizing Static Scene Components - RQ 13 -- 5.4 Visualizing Dynamic Scene Components - RQs 14, 15, 16 -- 5.5 Visualizing User Location and Activity - RQs 11, 12 -- 5.6 Referencing Scene Components - RQs 3, 4, 7, 8 -- 5.7 Manipulating Dynamic Scene Components - RQs 5, 6, 9, 10 -- 5.8 Scalable Interaction Techniques - RQs 17, 18, 20 -- 6 XRS Framework: Walkthrough -- 6.1 Collaborative Prototyping -- 6.2 Training and Teleoperation -- 7 Conclusion -- References. A Holistic Framework for Factory Planning Using Reinforcement Learning -- 1 Introduction -- 2 State of the Art -- 2.1 Introduction to Factory Layout Planning -- 2.2 Approaches for the Early Phase of Factory Layout Planning -- 2.3 Introduction to Reinforcement Learning -- 3 Research Gap -- 4 Framework for Factory Layout Planning Using Reinforcement Learning -- 4.1 Requirements -- 4.2 Description of the Framework -- 5 Step 5: Manual Planning -- 5.1 Evaluation of the Framework -- 6 Conclusion and Outlook -- References -- Simulation-Based Investigation of the Distortion of Milled Thin-Walled Aluminum Structural Parts Due to Residual Stresses -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 3.1 Initial Bulk Residual Stress Characterization -- 3.2 Machining Induced Residual Stress Characterization -- 3.3 Machining Induced Residual Stress as Driver for Distortion -- 3.4 Superposition of IBRS and MIRS and Its Effect on Distortion -- 4 Simulation Models -- 4.1 Distortion Prediction Model -- 4.2 Cutting Model to Predict the MIRS -- 5 Development of Compensation Techniques -- 6 Summary -- References -- Prediction of Thermodynamic Properties of Fluids at Extreme Conditions: Assessment of the Consistency of Molecular-Based Models -- 1 Introduction -- 2 Methods -- 2.1 Brown's Characteristic Curves -- 2.2 Substances -- 2.3 Molecular Simulation -- 2.4 Molecular-Based Equation of States -- 3 Results -- 3.1 Lennard-Jones Fluids -- 3.2 Mie Fluids -- 3.3 Toluene, Ethanol, and Dimethyl Ether -- 4 Conclusions -- References -- A Methodology for Developing a Model for Energy Prediction in Additive Manufacturing Exemplified by High-Speed Laser Directed Energy Deposition -- 1 Introduction -- 2 State of the Art -- 2.1 High-Speed Laser Directed Energy Deposition as an Additive Manufacturing Process -- 2.2 Current Discussion of the Environmental Impact of DED. 2.3 Requirements -- 3 Approach for Creating an Energy Prediction Model -- 3.1 Capturing the Structure -- 3.2 Process Analysis -- 3.3 Analysis of the Process Parameters -- 3.4 Creating the Model -- 4 Example of an Application Using HS DED-LB -- 4.1 Capturing the Structure -- 4.2 Process Analysis -- 4.3 Analysis of the Process Parameters -- 4.4 Creating the Model -- 4.5 Exemplary Application and Validation -- 5 Conclusion -- References -- Framework to Improve the Energy Performance During Design for Additive Manufacturing -- 1 Introduction -- 2 Research Background -- 2.1 Energy Performance Issues in Additive Manufacturing -- 2.2 Research Target and Tasks for This Work -- 3 Framework of Energy Performance Improvement in DfAM -- 3.1 Overview of the Framework -- 3.2 Structural Topology Optimization -- 3.3 Tool-Path Length Assessment -- 3.4 Multi-player Competition Algorithm -- 4 Use Cases -- 4.1 Use Case 1: 2D Optimization Problem -- 4.2 Use Case 2: 3D Optimization Problem -- 5 Discussion -- 6 Conclusion and Outlook -- References -- Investigation of Micro Grinding via Kinematic Simulations -- 1 Introduction -- 2 Properties of the MPGTs -- 3 Model of the MPGT for Kinematic Simulations -- 3.1 Analysis of the Grit Size Distribution -- 3.2 Analysis of the Grit Shape -- 3.3 Requirements and Assumptions for the Tool Model -- 3.4 Modeling of the Virtual Bond of the Tool Model -- 3.5 Validation of the Bond Thickness -- 3.6 Modeling of the Abrasive Grits -- 3.7 Positioning of the Virtual Grit Representations on the Virtual Tool -- 3.8 Evaluation of the Grit Size on the Real Tool -- 3.9 Adaption of the Grit Sizes for the Tool Model -- 3.10 Conclusion on Tool Modeling -- 4 Setup of the Simulation -- 4.1 Workpiece Representation Within the Simulation -- 4.2 Kinematics and Time Discretization -- 4.3 Calculation of the Tool-Workpiece Intersection. 5 Application of the Simulation Model to the Investigation of Micro Grinding -- 5.1 Influence of the Feed Rate on the Resulting Surface Topography -- 5.2 Calculation of the Undeformed Chip Thickness -- 6 Conclusion and Outlook -- References -- Molecular Dynamics Simulation of Cutting Processes: The Influence of Cutting Fluids at the Atomistic Scale -- 1 Introduction -- 2 Methods -- 2.1 Simulation Scenario -- 2.2 Molecular Model -- 2.3 Definition of Observables -- 3 Results -- 3.1 Mechanical Properties -- 3.2 Workpiece Deformation -- 3.3 Lubrication and Formation of Tribofilm -- 3.4 Thermal Properties -- 3.5 Reproducibility -- 4 Conclusions -- References -- Visual Analysis and Anomaly Detection of Material Flow in Manufacturing -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Visualization -- 3 Discussion -- 4 Conclusion -- References -- Author Index. |
isbn |
9783031357794 9783031357787 |
callnumber-first |
T - Technology |
callnumber-subject |
T - General Technology |
callnumber-label |
T55 |
callnumber-sort |
T 255.4 260.8 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30625775 |
illustrated |
Not Illustrated |
oclc_num |
1390758028 |
work_keys_str_mv |
AT aurichjanc proceedingsofthe3rdconferenceonphysicalmodelingforvirtualmanufacturingsystemsandprocesses AT garthchristoph proceedingsofthe3rdconferenceonphysicalmodelingforvirtualmanufacturingsystemsandprocesses AT linkebarbaras proceedingsofthe3rdconferenceonphysicalmodelingforvirtualmanufacturingsystemsandprocesses |
status_str |
n |
ids_txt_mv |
(MiAaPQ)50030625775 (Au-PeEL)EBL30625775 (OCoLC)1390758028 |
carrierType_str_mv |
cr |
is_hierarchy_title |
Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. |
author2_original_writing_str_mv |
noLinkedField noLinkedField |
marc_error |
Info : MARC8 translation shorter than ISO-8859-1, choosing MARC8. --- [ 856 : z ] |
_version_ |
1792331071146164224 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>11145nam a22004453i 4500</leader><controlfield tag="001">50030625775</controlfield><controlfield tag="003">MiAaPQ</controlfield><controlfield tag="005">20240229073851.0</controlfield><controlfield tag="006">m o d | </controlfield><controlfield tag="007">cr cnu||||||||</controlfield><controlfield tag="008">240229s2023 xx o ||||0 eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783031357794</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9783031357787</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(MiAaPQ)50030625775</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(Au-PeEL)EBL30625775</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1390758028</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">MiAaPQ</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">MiAaPQ</subfield><subfield code="d">MiAaPQ</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">T55.4-60.8</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Aurich, Jan C.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham :</subfield><subfield code="b">Springer International Publishing AG,</subfield><subfield code="c">2023.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2023.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (305 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Intro -- Preface -- Acknowledgement -- Contents -- List of Contributors -- Discrete Filter and Non-Gaussian Noise for Fast Roughness Simulations with Gaussian Processes -- 1 Introduction -- 2 Background -- 2.1 Roughness Model with Gaussian Processes -- 2.2 Simulation of Rough Surfaces -- 2.3 Related Work -- 3 Gaussian Process Filter -- 3.1 Discrete Filter -- 3.2 Discrete Filter with FFT -- 4 Experiments -- 4.1 Timings of the Discrete Filter with SciPy and CuFFT -- 4.2 Benchmarking Discrete Filter -- 5 Applications -- 6 Conclusion -- References -- Phase Field Simulations for Fatigue Failure Prediction in Manufacturing Processes -- 1 Introduction -- 2 A Phase Field Model for Cyclic Fatigue -- 2.1 A Time-Cycle Transformation in the Phase Field Fatigue Model -- 3 Phase Field Model in the Context of Manufacturing Process -- 3.1 Application in the Cold Forging Process -- 3.2 Modeling Cold Forging Process Using Phase Field Method -- 3.3 Phase Field Fatigue Model in Cylindrical Coordinate System -- 3.4 Phase Field Simulation of Cold Forging Process -- 4 Conclusion -- References -- Embedding-Space Explanations of Learned Mixture Behavior -- 1 Introduction -- 2 Rangesets -- 2.1 Motivation -- 2.2 Rangeset Construction -- 2.3 Application to Process-Level -- 3 Decision Boundary Visualization -- 3.1 CoFFi -- 3.2 Chemical Classes in Latent Feature Space -- 3.3 Latent Features in Physicochemical Descriptor Space -- 4 Conclusion and Future Work -- References -- Insight into Indentation Processes of Ni-Graphene Nanocomposites by Molecular Dynamics Simulation -- 1 Introduction -- 2 Method -- 3 Ni Single Crystal -- 4 Ni Bi-crystal -- 5 Ni Polycrystal -- 6 Summary -- References -- Physical Modeling of Grinding Forces -- 1 Introduction -- 2 Experimental Investigation -- 2.1 Requirements for Performing Experiments -- 2.2 Preparations for the Scratch Tests.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.3 Performing Scratch Tests in Dry Conditions -- 2.4 Performing Scratch Tests in Wet Conditions -- 3 Development of the Grinding Model -- 3.1 Selection of the Suitable Material Model -- 3.2 Discretization Approaches -- 3.3 Simulative Integration of the Cooling Lubricants -- 4 Conclusion -- References -- Modeling and Implementation of a 5G-Enabled Digital Twin of a Machine Tool Based on Physics Simulation -- 1 Motivation -- 2 State of the Art -- 2.1 5G Communication Standard -- 2.2 Physics Simulation in Manufacturing -- 2.3 Digital Twin in Manufacturing -- 3 Modeling of the Architecture for 5G-Enabled Digital Twin -- 3.1 Objectives and Requirements -- 3.2 System Architecture -- 3.3 Interactions and Information Flow -- 4 Implementation -- 4.1 Real System -- 4.2 Communication System -- 4.3 Digital System -- 4.4 Benefits and Challenges -- 5 Summary and Outlook -- References -- A Human-Centered Framework for Scalable Extended Reality Spaces -- 1 Introduction -- 2 Background -- 2.1 Terminology -- 2.2 Developing Collaborative Extended Reality Applications -- 3 XRS Framework: Basic Concept -- 3.1 Scalable Extended Reality (XRS) -- 3.2 Context of Use -- 4 XRS Framework: Requirements -- 4.1 Functional Requirements -- 4.2 Non-functional Requirements -- 5 XRS Framework: Design Solution -- 5.1 Access Points and Data - RQs 1, 2, 17, 18 -- 5.2 Subscribing to Collaborators - RQs 11, 12, 19 -- 5.3 Visualizing Static Scene Components - RQ 13 -- 5.4 Visualizing Dynamic Scene Components - RQs 14, 15, 16 -- 5.5 Visualizing User Location and Activity - RQs 11, 12 -- 5.6 Referencing Scene Components - RQs 3, 4, 7, 8 -- 5.7 Manipulating Dynamic Scene Components - RQs 5, 6, 9, 10 -- 5.8 Scalable Interaction Techniques - RQs 17, 18, 20 -- 6 XRS Framework: Walkthrough -- 6.1 Collaborative Prototyping -- 6.2 Training and Teleoperation -- 7 Conclusion -- References.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">A Holistic Framework for Factory Planning Using Reinforcement Learning -- 1 Introduction -- 2 State of the Art -- 2.1 Introduction to Factory Layout Planning -- 2.2 Approaches for the Early Phase of Factory Layout Planning -- 2.3 Introduction to Reinforcement Learning -- 3 Research Gap -- 4 Framework for Factory Layout Planning Using Reinforcement Learning -- 4.1 Requirements -- 4.2 Description of the Framework -- 5 Step 5: Manual Planning -- 5.1 Evaluation of the Framework -- 6 Conclusion and Outlook -- References -- Simulation-Based Investigation of the Distortion of Milled Thin-Walled Aluminum Structural Parts Due to Residual Stresses -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 3.1 Initial Bulk Residual Stress Characterization -- 3.2 Machining Induced Residual Stress Characterization -- 3.3 Machining Induced Residual Stress as Driver for Distortion -- 3.4 Superposition of IBRS and MIRS and Its Effect on Distortion -- 4 Simulation Models -- 4.1 Distortion Prediction Model -- 4.2 Cutting Model to Predict the MIRS -- 5 Development of Compensation Techniques -- 6 Summary -- References -- Prediction of Thermodynamic Properties of Fluids at Extreme Conditions: Assessment of the Consistency of Molecular-Based Models -- 1 Introduction -- 2 Methods -- 2.1 Brown's Characteristic Curves -- 2.2 Substances -- 2.3 Molecular Simulation -- 2.4 Molecular-Based Equation of States -- 3 Results -- 3.1 Lennard-Jones Fluids -- 3.2 Mie Fluids -- 3.3 Toluene, Ethanol, and Dimethyl Ether -- 4 Conclusions -- References -- A Methodology for Developing a Model for Energy Prediction in Additive Manufacturing Exemplified by High-Speed Laser Directed Energy Deposition -- 1 Introduction -- 2 State of the Art -- 2.1 High-Speed Laser Directed Energy Deposition as an Additive Manufacturing Process -- 2.2 Current Discussion of the Environmental Impact of DED.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.3 Requirements -- 3 Approach for Creating an Energy Prediction Model -- 3.1 Capturing the Structure -- 3.2 Process Analysis -- 3.3 Analysis of the Process Parameters -- 3.4 Creating the Model -- 4 Example of an Application Using HS DED-LB -- 4.1 Capturing the Structure -- 4.2 Process Analysis -- 4.3 Analysis of the Process Parameters -- 4.4 Creating the Model -- 4.5 Exemplary Application and Validation -- 5 Conclusion -- References -- Framework to Improve the Energy Performance During Design for Additive Manufacturing -- 1 Introduction -- 2 Research Background -- 2.1 Energy Performance Issues in Additive Manufacturing -- 2.2 Research Target and Tasks for This Work -- 3 Framework of Energy Performance Improvement in DfAM -- 3.1 Overview of the Framework -- 3.2 Structural Topology Optimization -- 3.3 Tool-Path Length Assessment -- 3.4 Multi-player Competition Algorithm -- 4 Use Cases -- 4.1 Use Case 1: 2D Optimization Problem -- 4.2 Use Case 2: 3D Optimization Problem -- 5 Discussion -- 6 Conclusion and Outlook -- References -- Investigation of Micro Grinding via Kinematic Simulations -- 1 Introduction -- 2 Properties of the MPGTs -- 3 Model of the MPGT for Kinematic Simulations -- 3.1 Analysis of the Grit Size Distribution -- 3.2 Analysis of the Grit Shape -- 3.3 Requirements and Assumptions for the Tool Model -- 3.4 Modeling of the Virtual Bond of the Tool Model -- 3.5 Validation of the Bond Thickness -- 3.6 Modeling of the Abrasive Grits -- 3.7 Positioning of the Virtual Grit Representations on the Virtual Tool -- 3.8 Evaluation of the Grit Size on the Real Tool -- 3.9 Adaption of the Grit Sizes for the Tool Model -- 3.10 Conclusion on Tool Modeling -- 4 Setup of the Simulation -- 4.1 Workpiece Representation Within the Simulation -- 4.2 Kinematics and Time Discretization -- 4.3 Calculation of the Tool-Workpiece Intersection.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5 Application of the Simulation Model to the Investigation of Micro Grinding -- 5.1 Influence of the Feed Rate on the Resulting Surface Topography -- 5.2 Calculation of the Undeformed Chip Thickness -- 6 Conclusion and Outlook -- References -- Molecular Dynamics Simulation of Cutting Processes: The Influence of Cutting Fluids at the Atomistic Scale -- 1 Introduction -- 2 Methods -- 2.1 Simulation Scenario -- 2.2 Molecular Model -- 2.3 Definition of Observables -- 3 Results -- 3.1 Mechanical Properties -- 3.2 Workpiece Deformation -- 3.3 Lubrication and Formation of Tribofilm -- 3.4 Thermal Properties -- 3.5 Reproducibility -- 4 Conclusions -- References -- Visual Analysis and Anomaly Detection of Material Flow in Manufacturing -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Visualization -- 3 Discussion -- 4 Conclusion -- References -- Author Index.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="590" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. </subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Garth, Christoph.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Linke, Barbara S.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Aurich, Jan C.</subfield><subfield code="t">Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes</subfield><subfield code="d">Cham : Springer International Publishing AG,c2023</subfield><subfield code="z">9783031357787</subfield></datafield><datafield tag="797" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30625775</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |