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E-raamat: Architecting A Knowledge-Based Platform for Design Engineering 4.0

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  • Ilmumisaeg: 10-Feb-2022
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030905217
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 10-Feb-2022
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030905217

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"Design Engineering for Industry 4.0 (DE4.0) represents the 'human-cyber-physical view of the systems realization ecosystem “that is necessary to accommodate the drivers of Industry 4.0 (IoX) and provide an open ecosystem for the realization of complex systems. Seamless integration of digital threads and digital twins throughout the product design, the development and fulfillment lifecycle; the ability to accommodate diverse and rapidly changing technologies; and the mechanisms to facilitate the creation of new opportunities for the design of products, processes, services, and systems are some of the desired characteristics of DE4.0."

Jiao, R., Commuri, S. Panchal, J., Milisavljevic-Syed, J, Allen, J.K., Mistree, F. and Schaefer, D., "Design Engineering in the Age of Industry 4.0," ASME Journal of Mechanical Design, 143(7), 070801, 25 pages.

In keeping with the Design Engineering 4.0 construct the authors describe architecting a computer platform to support human designers make decisions associated with the realization of complex engineered systems. The platform is designed to facilitate end-to-end digital integration, customization and personalization, agile collaboration networks, open innovation, co-creation and crowdsourcing, product servitization and anything-as-a-service.

Recognizing that simulation models are abstractions of reality the authors opt for a satisficing strategy instead of an optimization strategy. They include fundamentals and then describe tools for architecting a knowledge-based platforms for decision support. Challenges associated with developing a computational platform for decision support for the realization of complex engineered systems in the context of Design Engineering 4.0 are identified. Constructs for formulating design decisions (e.g., selection, compromise, and coupled decisions), knowledge modelling schemes (e.g., ontologies and modular templates), diagrams for designing decision workflows (e.g., the PEI-X diagram), and some analytical methods for robust design under uncertainty are presented. The authors describe integrating the knowledge-based platform to architect a cloud-based platform for decision support promoting co-design and cloud-based design communication essential for mass collaboration and open innovation for Design Engineering 4.0.

This book is a valuable resource for researchers, design engineers, and others working on pushing the boundary of digitized manufacturing to include Design Engineering 4.0 principles in designing products, processes, and services.
1 Requirements and Architecture of the Decision Support Platform for Design Engineering 4.0
1(22)
1.1 Background: Design Decision Support in the Context of Industry 4.0
1(7)
1.1.1 Design Engineering 4.0 and the Industrial Brain
2(3)
1.1.2 Decisions and Decision Support in the Context of Design Engineering 4.0
5(3)
1.2 Requirements for a Design Decision Support Platform
8(4)
1.2.1 Knowledge Management and Reuse
8(1)
1.2.2 Formulation of Decisions and Decision Workflows
9(1)
1.2.3 Solution Space Exploration
10(1)
1.2.4 Uncertainty Management
11(1)
1.2.5 User/Activity Specific Decision Support
12(1)
1.3 Architecture and Functionalities of the Design Decision Support Platform
12(4)
1.4 Organization and Validation Strategy of the Monograph
16(3)
References
19(4)
2 Foundations for Design Decision Support in Model-Based Complex Engineered Systems Realization
23(24)
2.1 Primary Constructs in Decision-Based Design
23(9)
2.1.1 sDSP---The Selection Decision Support Problem
24(3)
2.1.2 cDSP---The Compromise Decision Support Problem
27(5)
2.2 Framework for Robust Decision-Making
32(3)
2.3 Utilizing PEI-X Diagrams to Design Decision Workflows
35(3)
2.4 Knowledge-Based Techniques for Decision Support
38(5)
2.4.1 Template-Based Knowledge Capture and Reuse
38(1)
2.4.2 Ontology-Based Knowledge Formalization
39(3)
2.4.3 Knowledge-Based Platform for Decision Support
42(1)
2.5 Theoretical Structure Validity
43(1)
2.6 Where We Are and What Comes Next?
44(1)
References
44(3)
3 Ontology for Decision Support Problem Templates
47(56)
3.1 Frame of Reference
47(1)
3.2 Ontology-Based Representation of the sDSP Template
48(17)
3.2.1 Requirements for Knowledge Modeling to Support Selection Decisions
49(1)
3.2.2 Information Model of Selection Decisions---The sDSP Template
49(5)
3.2.3 sDSP Template Ontology Development
54(5)
3.2.4 Test Example---Material Selection for a Light Switch Cover Plate
59(6)
3.3 Ontology-Based Representation of the cDSP Template
65(16)
3.3.1 Requirements for Knowledge Modeling to Support Compromise Decisions
67(1)
3.3.2 Information Model of Compromise Decisions---The cDSP Template
67(2)
3.3.3 Ontology Development for the cDSP Template
69(4)
3.3.4 Test Example---Designing a Pressure Vessel
73(8)
3.4 Ontology-Based Representation of Coupled Hierarchical Decisions
81(18)
3.4.1 Mathematical Model for Coupled Hierarchical Decisions
81(3)
3.4.2 Requirements for Knowledge Modeling to Support Hierarchical Decisions
84(1)
3.4.3 Ontology Development for Decision Hierarchies
84(6)
3.4.4 Test Example---Designing a Portal Frame
90(9)
3.5 Empirical Structural Validity
99(1)
3.6 Where We Are and What Comes Next?
100(1)
References
100(3)
4 A Platform for Decision Support in the Design of Engineered Systems (PDSIDES) and Design of a Hot Rod Rolling System Using PDSIDES
103(36)
4.1 Primary Constructs of PDSIDES
103(3)
4.2 Design of Platform PDSIDES
106(5)
4.2.1 Platform Overview
106(1)
4.2.2 Users and Working Scenarios
107(3)
4.2.3 Knowledge-Based Decision Support Modes
110(1)
4.3 Implementation of Platform PDSIDES
111(4)
4.4 Testing the Performance of PDSIDES---Hot Rod Rolling Example Problem
115(1)
4.5 Hot Rod Rolling System (HRRS) Design Problem
115(2)
4.6 Knowledge-Based Decision Support in the Design of HRRS
117(1)
4.7 Original Design
117(6)
4.8 Adaptive Design
123(5)
4.9 Variant Design
128(6)
4.10 Validation of PDSIDES
134(1)
4.10.1 Empirical Structural Validation
134(1)
4.10.2 Empirical Performance Validity
134(1)
4.11 Role of
Chapter 4 and Remarks on the Knowledge-Based Platform PDSIDES
135(1)
4.12 Where We Are and What Comes Next?
135(1)
References
136(3)
5 Knowledge-Based Meta-Design of Decision Workflows
139(28)
5.1 Frame of Reference
139(2)
5.2 Requirements for Meta-Design Process Hierarchies Model
141(2)
5.3 Ontology Development for Designing Decision Workflows
143(8)
5.4 Test Example: Design of Shell and Tube Heat Exchanger
151(13)
5.4.1 Design of Shell and Tube Heat Exchanger for Thermal System
151(1)
5.4.2 Using DSPT Palette Entities for the Shell and Tube Heat Exchanger Design
152(3)
5.4.3 Design Scenarios for Shell and Tube Heat Exchanger Process Templates
155(9)
5.5 Empirical Structural Validity
164(1)
5.6 Where We Are and What Comes Next?
164(1)
References
164(3)
6 Knowledge-Based Robust Design Space Exploration
167(46)
6.1 Frame of Reference
167(2)
6.2 Ontology-Based Representation of Systematic Design Space Exploration
169(22)
6.2.1 Requirements for Design Space Exploration
169(1)
6.2.2 Design Space Exploration Procedure
170(5)
6.2.3 Design Space Adjustment
175(1)
6.2.4 Ontology for Process of Design Space Exploration
176(6)
6.2.5 Test Example: Designing of Hot Rod Rolling Process Chain
182(9)
6.3 Ontology-Based Uncertainty Management in Designing Robust Decision Workflows
191(18)
6.3.1 Requirements for Uncertainty Management in Decision Workflows
191(1)
6.3.2 Procedure for Designing Robust Decision Workflows
192(3)
6.3.3 Ontology for Designing Robust Design Decision Workflows
195(5)
6.3.4 Test Example: Design of Hot Rod Rolling System
200(9)
6.4 Empirical Structural Validity
209(1)
6.5 Where We Are and What Comes Next?
209(1)
References
210(3)
7 Extending PDSIDES to CB-PDSIDES: New Opportunities in Design Engineering 4.0
213(26)
7.1 Summary of Monograph
213(5)
7.2 Cloud-Based Decision Support: Framework and Open Questions
218(10)
7.2.1 Architecture of Cloud-Based PDSIDES
219(2)
7.2.2 Service Modeling
221(2)
7.2.3 Service Customization
223(1)
7.2.4 Intelligent Service Composition
224(1)
7.2.5 Smart Service Provider-Seeker Matching
225(2)
7.2.6 Mechanism for Design Collaboration (Co-Design)
227(1)
7.3 Broader Applications
228(3)
7.3.1 Applications to Cyber-Biophysical Systems
228(2)
7.3.2 Applications to Cyber-Physical-Product/Material Systems
230(1)
7.3.3 Applications to Cyber-Physical-Manufacturing Systems
230(1)
7.3.4 Applications to Cyber-Physical-Social Systems
230(1)
7.4 CB-PDSIDES for Design Engineering 4.0
231(3)
7.5 Closing Comments
234(2)
References
236(3)
Index 239
Zhenjun Ming is an Assistant Professor of the School of Mechanical Engineering at Beijing Institute of Technology (BIT). He received his PhD in Mechanical Engineering and his Bachelors degree in Industrial Engineering, both from BIT. Zhenjun Ming's research interests include knowledge- -based systems, decision making in engineering design, collective intelligence and self-organizing systems. He devotes himself to merging Information Technology (IT) and Operation Technology (OT) to deliver useful, effective, and efficient tools and approaches for supporting human decisions in the design of cyber-physical-social systems. He is a winner of the "2015 NSF/ASME Student Design Essay Award". He has spent 20 months working with Professors Farrokh Mistree and Janet K. Allen as a visiting scholar at the University of Oklahoma (Norman), on a China Scholarship Council (CSC) sponsored project - Knowledge-Based Platform for Decision Support in the Design of Engineering Systems. He has publishedone monograph, ten journal papers and eight conference papers. Anand Balu Nellippallil is an Assistant Professor in the Department of Mechanical and Civil Engineering at Florida Institute of Technology (FIT). Anand directs the Systems Realization Laboratory at FIT. He received his Ph.D. in Mechanical Engineering from the University of Oklahoma (OU) in 2018. Anand received his M. Tech degree in Materials Science and Engineering from the Indian Institute of Technology, Bhubaneswar, India in 2014, and his B.Tech degree in Production Engineering from the Government Engineering College Thrissur, University of Calicut, India in 2012. Before joining Florida Tech, he worked as a Research Engineer II at the Center for Advanced Vehicular Systems (CAVS) in Mississippi State University. His current research interests are focused on the realization of evolving human-cyber-physical-manufacturing-social systems. Anand has received several scholarships and awards, namely, the2018 Provosts Dissertation Prize for outstanding dissertation in science and engineering at OU, the Gallogly College of Engineering Dissertation Excellence Award, the Frank Chuck Mechanical Engineering Scholarships (2016 and 2017), Paper of Distinction at the ASME Design Automation Conference, a silver medal from the Indian Institute of Technology (IIT), Bhubaneswar for best academic performance and a university first rank medal from the University of Calicut. Anand has co-authored one other research monograph anchored in his PhD dissertation titled: Architecting Robust Co-Design of Materials, Products, and Manufacturing Processes. Anand is a member of ASME. Ru Wang is an Assistant Professor of the School of Mechanical Engineering at Beijing Institute of Technology (BIT). He received his Ph.D. in Mechanical Engineering in June 2018 from BIT and got his B.E. and M.E. degree in Traffic Engineering and Vehicle Operation Engineering in 2011 and 2014 from Shandong Universityof Technology. Since December 2016 to November 2017, Ru joined the Systems Realization Laboratory at the University of Oklahoma (Norman) as a Visiting Scholar for one year and conducted a Joint PhD Program supported by BIT. He is an ASME member and the winner of the "2017 NSF/ASME Student Design Essay Award". His research interests include the management of complexity and uncertainty in decision-based design, intelligent design and knowledge engineering. He has co-authored sixteen journal papers, and five conference papers. Janet K. Allen holds the John and Mary Moore Chair of Engineering at the University of Oklahoma, Norman. She received her SB from the Massachusetts Institute of Technology and her PhD form the University of California, Berkeley. Her research focus is on managing the uncertainty which is inherent in simulation-based design. Her group was among the first to recognize that there are four types of uncertainty inherent in simulation-based design andto suggest that this uncertainty could be managed with robust design. Janet Allen and her research group have co-authored one textbook and three monographs and more than 300 technical articles. She is a member of several academic and professional organizations, and is a Fellow of the American Society of Mechanical Engineers, a Senior Member of the American Institute of Aeronautics and Astronautics and is an Honorary Member of the Mechanical Engineering Honor Society Pi Tau Sigma. Professor Allen co-directs the Systems Realization Laboratory @ OU with Professor Farrokh Mistree. Guoxin Wang is a Professor of School of Mechanical Engineering at Beijing Institute of Technology (BIT). He is a Senior Member of the Chinese Society of Mechanical Engineers. Professor Wang directs and has accomplished 30 projects from the National Nature Science Foundation of China, the National High-Tech. R&D Program, and the National and International Enterprise Research Foundation. He has published over 80 papers and two books. His research interests include knowledge-based engineering, model-based system engineering and reconfigurable manufacturing systems. Yan Yan is a Professor of the School of Mechanical Engineering at Beijing Institute of Technology (BIT). She is also the Dean of the Department of Human Resource at BIT. Professor Yan received her Bachelors degree in 1989 and her PhD in 2001, both of them are in Mechanical Engineering at BIT. Her research interests include digital platform for design and manufacturing, knowledge-based engineering, decision-based design, and artificial intelligence in design and manufacturing. She has accomplished tens of projects funded by the Chinese NSF, Beijing NSF, and Chinese Key R&D Programs. Professor Yan has received seven National Science and Technology Advance Awards, one National Teaching Award. She has been granted more than ten patents in China, published three textbooks and two monographs (in Chinese), published more than eighty journal papers (in Chinese and English). She is a member of the Teaching Steering Committee of Industrial Engineering Specialty of China Ministry of Education, a member of the Chinese Society of Mechanical Engineers, an expert of Advance Design and Manufacturing in the Chinese Key R&D Programs. Professor Farrokh Mistree holds the L. A. Comp Chair in the School of Aerospace and Mechanical Engineering at the University of Oklahoma in Norman, Oklahoma. He received his B. Tech (Hons) degree in Naval Architecture in 1967 from the Indian Institute of Technology, Kharagpur and his Ph.D. in Engineering from the University of California, Berkeley in 1974. He has co-authored two textbooks, four monographs and more than 400 technical papers. His current research focus is on collaboratively defining the emerging frontier for the intelligent evolving cyber-physical-social systems when the computational models are incomplete and inaccurate. He is aFellow of ASME, an Associate Fellow of AIAA, a Life Member of The Honor Society of Phi Kappa Phi and a Member of ASEE, RINA and SNAME. Professor Mistree co-directs the Systems Realization Laboratory @ OU with Professor Janet K. Allen in Industrial and Systems Engineering.