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E-grāmata: Cloud-Based Cyber-Physical Systems in Manufacturing

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  • Formāts: PDF+DRM
  • Izdošanas datums: 16-Nov-2017
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319676937
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Cloud-Based Cyber-Physical Systems in ManufacturingPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 16-Nov-2017
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319676937
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This book presents state-of-the-art research, challenges and solutions in the area of cloud-based cyber-physical systems (CPS) used in manufacturing. It provides a comprehensive review of the literature and an in-depth treatment of novel methodologies, algorithms and systems in the area of architecture design, cyber security, process planning, monitoring and control.

The book features detailed descriptions of how to derive solutions in a cloud environment where physical machines can be supported by cyber decision systems when engaged in real operations. It presents a range of novel ideas and is characterized by a balanced approach in terms of scope vs. depth and theory vs. applications. It also takes into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

Dedicated to the topic of cloud-based CPS and its practical applications in manufacturing, this book benefits readers from all manufacturing sectors, from system design to lifecycle engineering and from process planning to machine control. It also helps readers to understand the present challenges and future research directions towards factories of the future, helping them to position themselves strategically for career development.

Part I Literature Survey and Trends
1 Latest Advancement in Cloud Technologies
3(30)
1.1 Introduction to Cloud Computing
3(15)
1.1.1 Historical Evolution and Background
4(1)
1.1.2 Concept
4(8)
1.1.3 Technologies
12(2)
1.1.4 Cloud Platforms
14(1)
1.1.5 Tools
15(2)
1.1.6 Challenges
17(1)
1.2 Cloud Manufacturing
18(11)
1.2.1 Historical Evolution and Background
18(2)
1.2.2 Concept
20(4)
1.2.3 Technologies
24(3)
1.2.4 Research Initiatives
27(1)
1.2.5 Applications
28(1)
1.2.6 Challenges
28(1)
1.3 Conclusions
29(4)
References
29(4)
2 Latest Advancement in CPS and IoT Applications
33(30)
2.1 Introduction
33(2)
2.2 Key Enabling Technologies in CPS and IoT
35(5)
2.2.1 Wireless Sensor Network
36(1)
2.2.2 Could Technologies
36(1)
2.2.3 Big Data
37(1)
2.2.4 Industry 4.0
38(1)
2.2.5 RFID Technology
39(1)
2.3 Key Features and Characteristics of CPS and IoT
40(3)
2.4 Applications of CPS and IoT
43(15)
2.4.1 Service Oriented Architecture
43(2)
2.4.2 Could Manufacturing
45(3)
2.4.3 Cyber-Physical Production Systems
48(4)
2.4.4 IoT-Enabled Manufacturing System
52(4)
2.4.5 CPS in Cloud Environment
56(2)
2.5 Conclusions
58(5)
References
59(4)
3 Challenges in Cybersecurity
63(20)
3.1 Introduction
63(1)
3.2 Internet of Things
64(1)
3.3 Remote Equipment Control
65(2)
3.4 Security Concerns and Methods
67(6)
3.4.1 Security Concerns
67(1)
3.4.2 Security Methods and Architecture
68(4)
3.4.3 Cyber-Physical Systems
72(1)
3.5 Future Outlook
73(1)
3.6 Conclusions
74(9)
References
77(6)
Part II Cloud-Based Monitoring, Planning and Control in CPS
4 Machine Availability Monitoring and Process Planning
83(22)
4.1 Introduction
83(1)
4.2 Literature Review
84(4)
4.3 Concept of Distributed Process Planning
88(2)
4.4 Architecture Design of a Web-Based DPP
90(1)
4.5 Functional Analysis of Web-DPP
91(5)
4.5.1 Supervisory Planning
93(1)
4.5.2 Execution Control
93(2)
4.5.3 Operation Planning
95(1)
4.6 Web-DPP Prototype Implementing
96(1)
4.7 A Case Study
97(4)
4.8 Conclusions
101(4)
References
102(3)
5 Cloud-Enabled Distributed Process Planning
105(20)
5.1 Introduction
105(2)
5.2 Multi-tasking Machines and Mill-Turn Parts
107(4)
5.3 Methodology
111(5)
5.3.1 Machine Modes
112(1)
5.3.2 Machine Mode Transitions
113(1)
5.3.3 Setup Frames
113(1)
5.3.4 Setup Planning and Setup Merging
114(1)
5.3.5 New FBs and FB Network Generation
115(1)
5.4 Case Study
116(4)
5.5 Discussions
120(1)
5.6 Conclusions
121(4)
References
122(3)
6 Adaptive Machining Using Function Blocks
125(38)
6.1 Introduction
125(2)
6.2 Function Block Concept
127(5)
6.2.1 Function Blocks
127(1)
6.2.2 Function Block Types
127(3)
6.2.3 Execution of Function Block
130(1)
6.2.4 Internal Behaviour of Function Block
131(1)
6.3 Enriched Machining Features
132(11)
6.3.1 Machining Features
132(2)
6.3.2 Enriched Machining Features
134(6)
6.3.3 Generic Machining Process Sequencing
140(3)
6.4 Adaptive Machining Feature Sequencing
143(14)
6.4.1 Reachability-Based Approach
144(9)
6.4.2 Case Study
153(4)
6.5 Adaptive Setup Merging and Dispatching
157(4)
6.6 Conclusions
161(2)
References
161(2)
7 Condition Monitoring for Predictive Maintenance
163(32)
7.1 Introduction
163(2)
7.2 Fundamentals of Prognosis
165(1)
7.3 Prognostic Methods
166(10)
7.3.1 Physics-Based Models
167(1)
7.3.2 AI-Based Data-Driven Models
168(1)
7.3.3 Statistical Data-Driven Models
169(2)
7.3.4 Model-Based Approach
171(3)
7.3.5 Comparison of Prognostic Models
174(2)
7.4 Prognosis-as-a-Service in Cloud Manufacturing
176(10)
7.4.1 Benefits of Cloud-Enabled Prognosis
176(3)
7.4.2 Supporting Technologies
179(2)
7.4.3 Implementing Prognosis in the Cloud
181(2)
7.4.4 Prognosis Applications
183(3)
7.5 Challenges and Limitations
186(2)
7.6 Conclusions
188(7)
References
189(6)
Part III Sustainable Robotic Assembly in CPS Settings
8 Resource Efficiency Calculation as a Cloud Service
195(16)
8.1 Introduction
195(1)
8.2 Related Work
195(2)
8.3 System Overview
197(1)
8.4 Methodology and Implementation
197(6)
8.4.1 Denavit-Hartenberg (D-H) Notation
198(1)
8.4.2 Forward Kinematics
198(1)
8.4.3 Inverse Kinematics
199(2)
8.4.4 Inverse Dynamics
201(1)
8.4.5 Energy Consumption
202(1)
8.4.6 Energy Optimisation
202(1)
8.5 Case Studies
203(4)
8.5.1 Energy Map of Robot Workspace
203(1)
8.5.2 Energy Measurement in Predefined Paths
203(4)
8.6 Conclusions
207(4)
References
207(4)
9 Safety in Human-Robot Collaborative Assembly
211(32)
9.1 Introduction
211(2)
9.2 Human Robot Collaboration
213(4)
9.3 Depth Sensor-Driven Active Collision Avoidance
217(10)
9.3.1 Kinect Sensors Calibration
217(1)
9.3.2 Depth Image Capturing
218(1)
9.3.3 Depth Image Processing
219(3)
9.3.4 Minimum Distance Calculation
222(1)
9.3.5 Active Collision Avoidance
222(3)
9.3.6 Velocity Detection
225(2)
9.4 System Verification
227(2)
9.5 A Remote Assembly Application
229(10)
9.5.1 System Configuration
229(2)
9.5.2 System Implementation
231(4)
9.5.3 Case Study
235(4)
9.6 Conclusions
239(4)
References
240(3)
10 Cloud Robotics Towards a CPS Assembly System
243(18)
10.1 Introduction
243(1)
10.2 Cloud Robotics
244(3)
10.2.1 Cloud Robotics at System Level
244(2)
10.2.2 Cloud Robotics at Application Level
246(1)
10.3 ICMS: an Example of Cloud Robotics System
247(5)
10.3.1 Integration Mechanisms in ICMS
248(2)
10.3.2 Cloud Robotic Application
250(2)
10.4 Implementation and Case Studies
252(4)
10.4.1 Cloud-Based Manufacturing Chain
252(2)
10.4.2 Human-Robot Collaboration
254(1)
10.4.3 Minimisation of Robot Energy Consumption
255(1)
10.5 Conclusions
256(5)
References
257(4)
11 Context-Aware Human-Robot Collaborative Assembly
261(36)
11.1 Introduction
261(1)
11.2 Gesture Recognition
262(15)
11.2.1 Gesture Recognition for Human-Robot Collaboration
263(1)
11.2.2 Sensor Technologies
263(4)
11.2.3 Gesture Identification
267(3)
11.2.4 Gesture Tracking
270(3)
11.2.5 Gesture Classification
273(3)
11.2.6 Future Trends of Gesture Recognition
276(1)
11.3 Human Motion Prediction
277(7)
11.3.1 Assembly Tasks Sequence
277(3)
11.3.2 HMM Human Motion Prediction
280(2)
11.3.3 Experiment
282(2)
11.3.4 Discussions
284(1)
11.4 AR-Based Worker Support System
284(5)
11.4.1 System Architecture
285(2)
11.4.2 AR Assembly Information Registrar
287(1)
11.4.3 Case Study
288(1)
11.5 Conclusions
289(8)
References
290(7)
Part IV CPS Systems Design and Lifecycle Analysis
12 Architecture Design of Cloud CPS in Manufacturing
297(28)
12.1 Introduction
297(3)
12.1.1 State-of-the-Art Cloud Manufacturing Approaches
298(1)
12.1.2 Supporting Technologies for Cloud Manufacturing
299(1)
12.1.3 Recap
300(1)
12.2 Cloud Manufacturing Framework
300(9)
12.2.1 Manufacturing Capability and Manufacturing Resource
301(3)
12.2.2 Cloud Architecture
304(5)
12.3 Interoperability and Other Issues
309(5)
12.3.1 Standardised File Formats
309(1)
12.3.2 STEP/STEP-NC to Bridge the Gap
310(2)
12.3.3 Approaches to Achieving Product Information Sharing
312(2)
12.4 Standardisation for Cloud Manufacturing
314(4)
12.5 Conclusions
318(7)
References
320(5)
13 Product Tracking and WEEE Management
325(22)
13.1 Introduction
325(3)
13.2 System Framework
328(4)
13.2.1 System Requirements and Roles
328(3)
13.2.2 WR2Cloud: System Framework
331(1)
13.3 Product Tracking Mechanism
332(4)
13.3.1 WR2Cloud
333(2)
13.3.2 `Cloud + QR'-based Tracking Methodology
335(1)
13.4 Implementations and Case Studies
336(4)
13.4.1 Case Study 1: Cloud WEEE Management at Product Level
336(2)
13.4.2 Case Study 2: Cloud-based WEEE Management at National/International Level
338(2)
13.5 Conclusions
340(7)
References
344(3)
14 Big Data Analytics for Scheduling and Machining
347(30)
14.1 Introduction
347(1)
14.1.1 Algorithms Used in Big Data Analytics
347(1)
14.1.2 Tools Used in Big Data Analytics
348(1)
14.2 Background Information
348(4)
14.2.1 Related Works on Scheduling
348(2)
14.2.2 Related Works on Machining Optimisation
350(1)
14.2.3 Big Data Analytics Application
351(1)
14.3 Big Data Analytics for Shop-Floor Scheduling
352(9)
14.3.1 Big Data Analytics Based Fault Prediction in Scheduling
352(1)
14.3.2 System Architecture
353(6)
14.3.3 A Simplified Case Study
359(2)
14.4 Big Data Analytics Based Optimisation for Machining
361(10)
14.4.1 Analysis of Machining Process
361(1)
14.4.2 Enriched Distributed Process Planning (DPP)
362(1)
14.4.3 Solution Strategy of Enriched DPP
363(2)
14.4.4 A Simplified Case Study
365(6)
14.5 Conclusions
371(6)
References
372(5)
15 Outlook of Cloud, CPS and IoT in Manufacturing
377(22)
15.1 Introduction
377(2)
15.2 Drivers, Barriers and Initiatives
379(2)
15.3 Characteristics and Requirements
381(7)
15.3.1 Systems of Systems (SoS)
384(1)
15.3.2 Internet of Things (IoT)
384(1)
15.3.3 Cloud Manufacturing (CM)
385(1)
15.3.4 Big Data
385(1)
15.3.5 Industry 4.0
386(1)
15.3.6 Challenges
387(1)
15.3.7 Discussions
388(1)
15.4 Representative Examples of CPS in Manufacturing
388(6)
15.4.1 Example 1: Service-Oriented Architecture
388(3)
15.4.2 Example 2: Cloud Manufacturing
391(1)
15.4.3 Example 3: Adaptive Manufacturing Systems
391(2)
15.4.4 Example 4: Model-Driven Manufacturing Systems
393(1)
15.5 Future Research Directions
394(2)
15.6 Conclusions
396(3)
References
396(3)
Index 399
Professor Lihui Wang is currently Chair of Sustainable Manufacturing at the KTH Royal Institute of Technology, Sweden. Contributing to cutting-edge academic research, highly qualified personnel training, technologies transfer to industries, and the advancement of engineering science and manufacturing, Professor Lihui Wang holds a PhD in Intelligence Science from Kobe University, Kobe, Japan, and has been the recipient of numerous awards including the Best Paper Award at the International Conference on FAIM, Dresden, Germany, July 2002 and the Best Organiser of Symposium and Sessions (BOSS) Award at the ASME Manufacturing Science and Engineering Conference, Detroit, USA, June 2014, amongst others.

He is currently Editor-in-Chief of the International Journal of Manufacturing Research and Editor for the Journal of Intelligent Manufacturing, Robotics and Computer-Integrated Manufacturing, and an Editorial Board Member of the Chinese Journal of Mechanical Engineering and the International Journal of Computer Integrated Manufacturing, amongst others.

Professor Lihui Wang has published over 130 refereed journal papers and is author of more than 7 books and 15 book chapters. He is also an associate member of the International Academy for Production Engineering (CIRP).

Xi Vincent Wang is currently a Postdoctoral Research fellow in Production Systems, Cloud Manufacturing and Robotics/Cyber Physical Systems at the KTH Royal Institute of Technology, Sweden.

He is Managing Editor of the International Journal of Manufacturing Research and serves as Project Manager of the EU SYMBIO-TIC project.





Holding a PhD in Mechanical Engineering from the University of Auckland and a Bachelor of Engineering from Tianjin University, Xi Vincent Wang has published numerous journal articles and book chapters.
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