"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.
Author(s): Zhenjun Ming, Anand Balu Nellippallil, Ru Wang, Janet K. Allen, Guoxin Wang, Yan Yan, Farrokh Mistree
Publisher: Springer
Year: 2022
Language: English
Pages: 253
City: Cham
Foreword
Architecting Knowledge-Based Platform for Design Engineering 4.0
Preface
Contents
1 Requirements and Architecture of the Decision Support Platform for Design Engineering 4.0
1.1 Background: Design Decision Support in the Context of Industry 4.0
1.1.1 Design Engineering 4.0 and the Industrial Brain
1.1.2 Decisions and Decision Support in the Context of Design Engineering 4.0
1.2 Requirements for a Design Decision Support Platform
1.2.1 Knowledge Management and Reuse
1.2.2 Formulation of Decisions and Decision Workflows
1.2.3 Solution Space Exploration
1.2.4 Uncertainty Management
1.2.5 User/Activity Specific Decision Support
1.3 Architecture and Functionalities of the Design Decision Support Platform
1.4 Organization and Validation Strategy of the Monograph
References
2 Foundations for Design Decision Support in Model-Based Complex Engineered Systems Realization
2.1 Primary Constructs in Decision-Based Design
2.1.1 sDSP—The Selection Decision Support Problem
2.1.2 cDSP—The Compromise Decision Support Problem
2.2 Framework for Robust Decision-Making
2.3 Utilizing PEI-X Diagrams to Design Decision Workflows
2.4 Knowledge-Based Techniques for Decision Support
2.4.1 Template-Based Knowledge Capture and Reuse
2.4.2 Ontology-Based Knowledge Formalization
2.4.3 Knowledge-Based Platform for Decision Support
2.5 Theoretical Structure Validity
2.6 Where We Are and What Comes Next?
References
3 Ontology for Decision Support Problem Templates
3.1 Frame of Reference
3.2 Ontology-Based Representation of the sDSP Template
3.2.1 Requirements for Knowledge Modeling to Support Selection Decisions
3.2.2 Information Model of Selection Decisions—The sDSP Template
3.2.3 sDSP Template Ontology Development
3.2.4 Test Example—Material Selection for a Light Switch Cover Plate
3.3 Ontology-Based Representation of the cDSP Template
3.3.1 Requirements for Knowledge Modeling to Support Compromise Decisions
3.3.2 Information Model of Compromise Decisions—The cDSP Template
3.3.3 Ontology Development for the cDSP Template
3.3.4 Test Example—Designing a Pressure Vessel
3.4 Ontology-Based Representation of Coupled Hierarchical Decisions
3.4.1 Mathematical Model for Coupled Hierarchical Decisions
3.4.2 Requirements for Knowledge Modeling to Support Hierarchical Decisions
3.4.3 Ontology Development for Decision Hierarchies
3.4.4 Test Example—Designing a Portal Frame
3.5 Empirical Structural Validity
3.6 Where We Are and What Comes Next?
References
4 A Platform for Decision Support in the Design of Engineered Systems (PDSIDES) and Design of a Hot Rod Rolling System Using PDSIDES
4.1 Primary Constructs of PDSIDES
4.2 Design of Platform PDSIDES
4.2.1 Platform Overview
4.2.2 Users and Working Scenarios
4.2.3 Knowledge-Based Decision Support Modes
4.3 Implementation of Platform PDSIDES
4.4 Testing the Performance of PDSIDES—Hot Rod Rolling Example Problem
4.5 Hot Rod Rolling System (HRRS) Design Problem
4.6 Knowledge-Based Decision Support in the Design of HRRS
4.7 Original Design
4.8 Adaptive Design
4.9 Variant Design
4.10 Validation of PDSIDES
4.10.1 Empirical Structural Validation
4.10.2 Empirical Performance Validity
4.11 Role of Chapter 4 and Remarks on the Knowledge-Based Platform PDSIDES
4.12 Where We Are and What Comes Next?
References
5 Knowledge-Based Meta-Design of Decision Workflows
5.1 Frame of Reference
5.2 Requirements for Meta-Design Process Hierarchies Model
5.3 Ontology Development for Designing Decision Workflows
5.4 Test Example: Design of Shell and Tube Heat Exchanger
5.4.1 Design of Shell and Tube Heat Exchanger for Thermal System
5.4.2 Using DSPT Palette Entities for the Shell and Tube Heat Exchanger Design
5.4.3 Design Scenarios for Shell and Tube Heat Exchanger Process Templates
5.5 Empirical Structural Validity
5.6 Where We Are and What Comes Next?
References
6 Knowledge-Based Robust Design Space Exploration
6.1 Frame of Reference
6.2 Ontology-Based Representation of Systematic Design Space Exploration
6.2.1 Requirements for Design Space Exploration
6.2.2 Design Space Exploration Procedure
6.2.3 Design Space Adjustment
6.2.4 Ontology for Process of Design Space Exploration
6.2.5 Test Example: Designing of Hot Rod Rolling Process Chain
6.3 Ontology-Based Uncertainty Management in Designing Robust Decision Workflows
6.3.1 Requirements for Uncertainty Management in Decision Workflows
6.3.2 Procedure for Designing Robust Decision Workflows
6.3.3 Ontology for Designing Robust Design Decision Workflows
6.3.4 Test Example: Design of Hot Rod Rolling System
6.4 Empirical Structural Validity
6.5 Where We Are and What Comes Next?
References
7 Extending PDSIDES to CB-PDSIDES: New Opportunities in Design Engineering 4.0
7.1 Summary of Monograph
7.2 Cloud-Based Decision Support: Framework and Open Questions
7.2.1 Architecture of Cloud-Based PDSIDES
7.2.2 Service Modeling
7.2.3 Service Customization
7.2.4 Intelligent Service Composition
7.2.5 Smart Service Provider-Seeker Matching
7.2.6 Mechanism for Design Collaboration (Co-Design)
7.3 Broader Applications
7.3.1 Applications to Cyber-Biophysical Systems
7.3.2 Applications to Cyber-Physical-Product/Material Systems
7.3.3 Applications to Cyber-Physical-Manufacturing Systems
7.3.4 Applications to Cyber-Physical-Social Systems
7.4 CB-PDSIDES for Design Engineering 4.0
7.5 Closing Comments
References
Index
