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

Bibliographic Details
Main Author: Aurich, Jan C.
Other Authors: Garth, Christoph., Linke, Barbara S.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2023.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
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.

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