Ni / Majstorovic / Djurdjanovic Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing

AMP 2018
1. Auflage 2018
ISBN: 978-3-319-89563-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 321 Seiten, eBook

Reihe: Lecture Notes in Mechanical Engineering

This book presents the proceedings of the 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing (AMP 2018), held in Belgrade, Serbia, on 5–7 June 2018, the latest in a series of high-level conferences that brings together experts from academia and industry to exchange knowledge, ideas, experiences, research findings, and information in the field of manufacturing. The book addresses a wide range of topics, including, for example, design of smart and intelligent products, developments in CAD/CAM technologies, rapid prototyping and reverse engineering, multistage manufacturing processes, manufacturing automation in the Industry 4.0 model, cloud-based products, and cyber-physical and reconfigurable manufacturing systems. By providing updates on key issues and recent advances in manufacturing engineering and technologies, it aids the transfer of vital knowledge to the next generation of academics and practitioners. It appeals to anyone working or conducting research in this rapidly evolving field.

Zielgruppe

Research

Autoren/Hrsg.

Ni, Jun

Majstorovic, Vidosav D.

Djurdjanovic, Dragan

Inhaltsverzeichnis

1;Preface;6
1.1;Conference Founder and Chair;7
1.2;Conference Co-chairs;7
1.3;International Programme Committee;7
1.4;Organizing Committee;8
2;Contents;10
3;Hybrid Machining Processes;12
3.1;Abstract;12
3.2;1 Introduction;12
3.3;2 Classification of Hybrid Processes;13
3.4;3 Assisted Hybrid Machining Processes;14
3.4.1;3.1 High-Pressure Jet Assisted Machining (HPJAM);14
3.4.2;3.2 Cryogenic Machining;19
3.4.3;3.3 Laser Assisted Machining;22
3.4.4;3.4 Ultrasonic Assisted Machining;24
3.5;4 Conclusions;26
3.6;References;27
4;Intelligent Sensing Systems – Status of Research at KaProm;29
4.1;Abstract;29
4.2;1 Introduction;29
4.3;2 The Structure of Intelligent Sensing System;31
4.4;3 Machining Operations Monitoring;32
4.5;4 2D and 3D Vision Systems;34
4.5.1;4.1 Identification of Textile Web Lateral Density Profile;35
4.5.2;4.2 Image-Based Visual Servo Control of Robot Manipulator;36
4.5.3;4.3 Recognition of Planar Segments from 3D Point Clouds;38
4.6;5 Contact States Recognition in Assembly Processes;39
4.7;6 Sensing Systems for Mobile Robots;41
4.7.1;6.1 Low-Cost Mobile Robot Positioning Based on Infrared Sensors;41
4.7.2;6.2 Biologically Inspired Algorithms for Learning and Reproduction of Mobile Robot Motion Trajectories;43
4.8;7 Conclusion;45
4.9;Acknowledgments;46
4.10;References;46
5;Industry 4.0 and Paradigm Change in Economics and Business Management;48
5.1;Abstract;48
5.2;1 Introduction;48
5.3;2 Industrial Revolution 4.0: The Impact on an Economy;49
5.4;3 New Social Normalities in Industry 4.0;52
5.5;4 Paradigm Change in Economic Theory and Policy Platform;56
5.6;5 Serbia’s Road of New Industrialization;60
5.7;6 Industrial Policy and Advanced Manufacturing;62
5.8;7 Conclusion;66
5.9;References;66
6;Photogrammetry Applied to Small and Micro Scaled Objects: A Review;68
6.1;Abstract;68
6.2;1 Introduction;68
6.3;2 Photogrammetric Process Applied to Small Objects;69
6.4;3 Main Advances Regarding the Photogrammetric Reconstruction of Small Objects;71
6.4.1;3.1 Camera Calibration;71
6.4.2;3.2 Image Acquisition;74
6.4.2.1;3.2.1 Automated Scanners with Moving Sensors (Motorized Systems);74
6.4.2.2;3.2.2 Scanning Strategy;75
6.4.2.3;3.2.3 Illumination;76
6.4.3;3.3 Image Processing;77
6.4.3.1;3.3.1 Software Algorithms;77
6.4.3.2;3.3.2 Scale Adjustment;79
6.5;4 Measurement Traceability;81
6.6;5 Future Challenges;85
6.7;6 Conclusions;86
6.8;References;86
7;Artificial Neural Networks in Advanced Thermoset Matrix Composite Manufacturing;89
7.1;Abstract;89
7.2;1 Introduction;89
7.3;2 Materials and Methods;90
7.3.1;2.1 Modelling;90
7.3.2;2.2 Artificial Neural Network Model;92
7.4;3 Results and Discussion;94
7.5;4 Conclusions;98
7.6;References;98
8;Recent Streams of Digital Manufacturing, Its Emerging Trend and Future Directions for SME in Japan;100
8.1;Abstract;100
8.2;1 Introduction;100
8.3;2 Background;101
8.4;3 Findings;102
8.5;4 Discussions;104
8.6;References;108
9;Intelligent Integrated Management and Advanced Metrology for Quality Toward the Factory of the Future;109
9.1;Abstract;109
9.2;1 Introduct?on;109
9.3;2 Intelligent integrated Management and Metrology for Design and Production;111
9.4;3 The Model of Precision Manufacturers Based on Intelligent Metrology and Geometrical Product Specif ...;113
9.5;4 Linking of Intelligent Integrated Management to Design and Advanced Production;114
9.6;5 Intelligent Metrology and Quality Assurance;115
9.7;6 Conclusion and Future Work;118
9.8;References;118
10;Hybrid Machining: An Industrial Case-Study Comparing Inconel718 Reaming and Drilling with Abrasive W ...;120
10.1;Abstract;120
10.2;1 Introduction;120
10.2.1;1.1 Background;120
10.3;2 Approach;121
10.4;3 Process Methodology;121
10.5;4 Experimental Setup;122
10.6;5 Results and Discussion;123
10.7;6 Conclusions;124
10.8;Acknowledgments;125
10.9;References;125
11;Dynamic Definition of Machine Tool Feed Drive Models in Advanced Machine Tools;126
11.1;Abstract;126
11.2;1 General Aspects of Machine Tool Dynamics;126
11.3;2 Estimated and Measurable Behavior of Machine Tool;127
11.4;3 Stiffness Modeling;129
11.4.1;3.1 Stiffness Modeling of Feed Drive;129
11.4.1.1;3.1.1 Method for Calculating the Ball Screw Stiffness;130
11.4.1.2;3.1.2 Ball Screw Stiffness Given by Catalogues;130
11.4.1.3;3.1.3 Variable Ballscrew Stiffness;131
11.4.2;3.2 Nut Stiffness;132
11.4.3;3.3 Influence of Bearing Stiffness;135
11.4.3.1;3.3.1 Relationship Between Axial and Radial Stiffness of Bearings Given by Design Experience;138
11.4.3.2;3.3.2 Bearing Stiffness Given by Product Catalogs;138
11.4.3.3;3.3.3 Experimental Determination of Bearing Stiffness;139
11.4.4;3.4 Guideways Stiffness;140
11.4.4.1;3.4.1 Linear Rolling Guide Model;140
11.4.4.2;3.4.2 Stiffness Given by Catalogues;141
11.5;4 Damping Modeling;142
11.6;5 Determination of Dynamic Parameters of Feed Drives;146
11.6.1;5.1 Static Stiffness Measurement;146
11.6.2;5.2 Dynamic Characteristic Identification;147
11.7;6 Conclusions;147
11.8;References;147
12;Strategic Outsourcing of SMEs in the Context of Industry 4.0: Evidence from Serbia;150
12.1;Abstract;150
12.2;1 Introduction;150
12.3;2 Theoretical Background;151
12.3.1;2.1 Industry 4.0;151
12.3.2;2.2 Strategic Outsourcing;151
12.3.3;2.3 Research Question;151
12.4;3 Data and Methodology;152
12.5;4 Results and Discussion;152
12.6;5 Conclusion;155
12.7;References;155
13;Fundamental Requirements for CAPP Software Design Focusing on Industry 4.0 Specific Features;157
13.1;Abstract;157
13.2;1 Introduction;157
13.3;2 European and Slovak Industrial Markets Analysis;158
13.4;3 Requirements for Process Plans Design Within Industry 4.0;161
13.5;4 Conclusion;164
13.6;Acknowledgments;165
13.7;References;166
14;Strategic Note for a Digital Innovation Policy;167
14.1;Abstract;167
14.2;1 The Rise of Industry 4.0;167
14.3;2 Policy Aspects vs Middle-Course Tasks;170
14.4;3 Building Awareness;171
14.5;4 Thematic Cooperation;171
14.6;5 Synergies and Complementarities;171
14.7;6 People Focus;172
14.8;7 Case Studies;172
14.9;8 Priorities;172
14.10;9 Concluding Remarks;173
14.11;Acknowledgments;173
14.12;References;173
15;Machining Process Improvement Through Process Twins;175
15.1;Abstract;175
15.2;1 Introduction;175
15.3;2 Modeling of Machining;176
15.3.1;2.1 Cutting Models Used for Process Twins;176
15.3.2;2.2 Machining Stability and Process Twins;178
15.4;3 Machining Cycle Simulation Using the Process Twin;182
15.4.1;3.1 Modeling of Tool Geometry and Tool Motion;182
15.5;4 Industrial Applications of Process Twins;183
15.5.1;4.1 Application to Die Machining;183
15.5.2;4.2 Five Axis Milling of Complex Geometries Using the Process Twin;185
15.5.3;4.3 Stability of Thin-Walled Part Machining Through Process Twin;185
15.5.4;4.4 Process-Tool Twins for Broaching;187
15.6;5 Conclusions;188
15.7;References;189
16;Connecting Humans to the Loop of Digitized Factories’ Automation Systems;191
16.1;Abstract;191
16.2;1 Introduction;191
16.3;2 Human-Automation Interaction Challenges;193
16.4;3 A Reference Framework for Human-Automation Interaction;194
16.4.1;3.1 System Design;196
16.4.2;3.2 System Operation;197
16.5;4 Framework Application in Industrial Cases;198
16.5.1;4.1 Design for Human-Centered Lines in the White-Goods Industry;198
16.5.2;4.2 Real-Time Support for Operators in the Furniture Industry;201
16.6;5 Conclusion;202
16.7;Acknowledgments;203
16.8;References;203
17;Development of Skills and Competences in Manufacturing Towards Education 4.0: A Teaching Factory App ...;205
17.1;Abstract;205
17.2;1 Introduction;205
17.3;2 Manufacturing Transformation to Industry 4.0;206
17.3.1;2.1 Traditional Manufacturing;206
17.3.2;2.2 Industry 4.0 Transition and Requirements for New Skills;207
17.3.2.1;2.2.1 Key Enabling Technologies for Industry 4.0;207
17.3.2.2;2.2.2 Introduction of Enabling Technologies to Industry 4.0 Workflow and Required Skills;208
17.4;3 Education Transformation to Industry 4.0;211
17.5;4 Innovative Teaching Factory Approach;214
17.6;5 Conclusions;217
17.7;References;218
18;Opportunistic Maintenance for Wind Turbines Considering External Opportunities – A Case Study;222
18.1;Abstract;222
18.2;1 Introduction;223
18.3;2 Model Summary;224
18.3.1;2.1 Determining {\varvec p}_{{{\varvec OM}}} , {\varvec p}_{{{\varvec CM}}} and {\varvec p}_{{{\varvec PM}}};225
18.3.2;2.2 Optimization Problem;227
18.4;3 Case Study of Wind Turbine;228
18.4.1;3.1 Failure Mechanism;228
18.4.2;3.2 Opportunity Model According to Wind Speed;229
18.4.3;3.3 Downtime Cost Rate;230
18.5;4 Results Analysis;233
18.5.1;4.1 Effect of Failure Cost;233
18.5.2;4.2 Effect of Production Loss Cost (Electricity Prices);234
18.5.3;4.3 Effect of Wind Threshold Defining Opportunity Occurrences;235
18.6;5 Conclusion;235
18.7;References;236
19;Cyber-Physical Manufacturing in Context of Industry 4.0 Model;238
19.1;Abstract;238
19.2;1 Introduction;238
19.3;2 Industry 4.0 Program – Facts and Figures;239
19.4;3 Industry 4.0 Program – World Wide Approaches;241
19.5;4 Industry 4.0 Program in Serbia;246
19.6;5 Cyber-Physical Manufacturing;247
19.7;6 Conclusions;247
19.8;References;248
20;An Automated Laser Control Technique for Improving Powder Bed Temperature Uniformity in Selective La ...;250
20.1;Abstract;250
20.2;1 Introduction;250
20.3;2 Thermal Model;252
20.4;3 Experimental Setup;254
20.5;4 Results;255
20.5.1;4.1 Baseline Fixed Power;255
20.5.2;4.2 Feed-Forward Laser Control;257
20.6;5 Conclusion;259
20.7;References;259
21;Gaussian Process Regression for Virtual Metrology of Microchip Quality and the Resulting Selective S ...;261
21.1;Abstract;261
21.2;1 Introduction;261
21.3;2 Bayesian Modeling Approach;263
21.3.1;2.1 Conceptual Background;263
21.3.2;2.2 Mathematical Foundation;264
21.3.2.1;2.2.1 Incorporation of Prior Knowledge;265
21.3.2.2;2.2.2 Tuning of Parameters;265
21.3.2.3;2.2.3 Prediction Under Uncertain Embedding;265
21.3.2.4;2.2.4 Active Learning of Embedding;266
21.4;3 Results;267
21.4.1;3.1 Description of Dataset;267
21.4.2;3.2 Description of Underlying Experiment;270
21.4.3;3.3 Learning Curves;271
21.5;4 Conclusions and Future Work;273
21.6;References;274
22;Cloud Enabled CPS and Big Data in Manufacturing;276
22.1;Abstract;276
22.2;1 Introduction;276
22.3;2 Literature Review;277
22.3.1;2.1 Cyber-Physical Systems;277
22.3.2;2.2 Machining Optimisation;278
22.3.3;2.3 Scheduling;279
22.4;3 Could CPS in Manufacturing;280
22.5;4 A Method for Cloud CPS Implementation;282
22.5.1;4.1 Concepts of Holons, Agents and Function Blocks;282
22.5.2;4.2 A Cloud CPS Architecture;282
22.5.3;4.3 Implementation;284
22.5.4;4.4 A Prototype: Wise-ShopFloor;287
22.6;5 Big Data Analytics Enhanced Cloud CPS;288
22.6.1;5.1 BDA Advantage in Manufacturing;288
22.6.2;5.2 BDA Enriched Distributed Process Planning (E-DPP);289
22.6.2.1;5.2.1 Problem Representation of E-DPP;290
22.6.2.2;5.2.2 A Simplified Case Study for E-DPP;291
22.6.3;5.3 BDA Enhanced Scheduling;294
22.6.3.1;5.3.1 BDA for Fault Prediction in Scheduling;294
22.6.3.2;5.3.2 System Architecture;294
22.6.3.3;5.3.3 A Simplified Case Study;297
22.7;6 Conclusions;298
22.8;References;298
23;Geometric Inspection Planning as a Key Element in Industry 4.0;304
23.1;1 What is Inspection Planning?;304
23.2;2 Standard Practices in Inspection Planning;307
23.3;3 Advanced Methodologies for Sampling Strategy Design;309
23.3.1;3.1 Definition of the Point Distribution;310
23.3.2;3.2 Choice of the Sample Size: Economic Aspects;312
23.3.3;3.3 Path Planning and Probe Configuration;313
23.4;4 The Role of Inspection in Industry 4.0;314
23.4.1;4.1 Inspection of Additive Manufacturing Parts;315
23.5;5 Conclusions;316
23.6;References;316