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|a 9783031357794
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|a T55.4-60.8
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|a Aurich, Jan C.
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|a Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes.
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|a 1st ed.
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|a Cham :
|b Springer International Publishing AG,
|c 2023.
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|c ©2023.
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|a 1 online resource (305 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
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|a online resource
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|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.
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|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.
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|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.
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|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.
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|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.
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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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| 700 |
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|a Garth, Christoph.
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| 700 |
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|a Linke, Barbara S.
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|i Print version:
|a Aurich, Jan C.
|t Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes
|d Cham : Springer International Publishing AG,c2023
|z 9783031357787
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| 797 |
2 |
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|a ProQuest (Firm)
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| 856 |
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=30625775
|z Click to View
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