Data-Driven Engineering Design - Liu, Ang.; Wang, Yuchen; Wang, Xingzhi; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2022)
This book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design. Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation. Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.
Data-driven Engineering Design -- User-Generated Content Analysis for Customer Needs Elicitation -- Data-driven Conceptual Design -- Management of Constraints, Complexities, and Contradictions in the Data Era -- Blockchain-based Data-driven Smart Customisation -- Data-driven Design of Smart Product -- Data-driven Smart Product Service System -- Digital Twin for Data-driven Engineering Design -- Enabling Technologies of Data-driven Engineering Design.
Keywords: Engineering design; Computer-aided engineering; Engineering Data processing; Industrial engineering; Production engineering; Engineering Design; Computer-Aided Engineering (CAD, CAE) and Design; Data Engineering; Industrial and Production Engineering; Engineering design -- Data processing