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Data-driven engineering design

Liu, Ang.

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Data-driven engineering design

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-driven engineering design/ by Ang Liu, Yuchen Wang, Xingzhi Wang.
作者:	Liu, Ang.
其他作者:	Wang, Yuchen.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	ix, 197 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Engineering design - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-88181-8
ISBN:	9783030881818

Data-driven engineering design
Liu, Ang.

Data-driven engineering design[electronic resource] /by Ang Liu, Yuchen Wang, Xingzhi Wang. - Cham :Springer International Publishing :2022. - ix, 197 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030881818

Standard No.: 10.1007/978-3-030-88181-8doiSubjects--Topical Terms:

528131
Engineering design
--Data processing.

LC Class. No.: TA174 / .L58 2022

Dewey Class. No.: 620.00420285

Data-driven engineering design
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505 0 $a 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.
520 $a 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.
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