Architecting A Knowledge-Based Platform for Design Engineering 4.0

"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… Lue lisää "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.