Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes.

• Author: Aurich, Jan C.
• TeilnehmendeR: Garth, Christoph.; Linke, Barbara S.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2023.; ©2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (305 pages)

Table of 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.