國立虎尾科技大學 |

Data-Driven Engineering Design

紀錄類型:	書目-語言資料,印刷品 : 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, Xingzhi.
面頁冊數:	IX, 197 p. 55 illus., 51 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Industrial and Production Engineering. -
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. - 1st ed. 2022. - IX, 197 p. 55 illus., 51 illus. in color.online resource.

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:

593943
Industrial and Production Engineering.

LC Class. No.: TA174

Dewey Class. No.: 620.0042

Data-Driven Engineering Design
LDR:02858nam a22003975i 4500 001 1089880
003 DE-He213
005 20220126151125.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030881818 $9 978-3-030-88181-8
024 7 $a 10.1007/978-3-030-88181-8 $2 doi
035 $a 978-3-030-88181-8
050 4 $a TA174
072 7 $a TBD $2 bicssc
072 7 $a TEC016020 $2 bisacsh
072 7 $a TBD $2 thema
082 0 4 $a 620.0042 $2 23
100 1 $a Liu, Ang. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1397172
245 1 0 $a Data-Driven Engineering Design $h [electronic resource] / $c by Ang Liu, Yuchen Wang, Xingzhi Wang.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a IX, 197 p. 55 illus., 51 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
650 2 4 $a Industrial and Production Engineering. $3 593943
650 2 4 $a Data Engineering. $3 1226308
650 2 4 $a Computer-Aided Engineering (CAD, CAE) and Design. $3 669928
650 1 4 $a Engineering Design. $3 670857
650 0 $a Production engineering. $3 566269
650 0 $a Industrial engineering. $3 679492
650 0 $a Engineering—Data processing. $3 1297966
650 0 $a Computer-aided engineering. $3 560192
650 0 $a Engineering design. $3 560518
700 1 $a Wang, Xingzhi. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1397173
700 1 $a Wang, Yuchen. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1228251
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030881801
776 0 8 $i Printed edition: $z 9783030881825
776 0 8 $i Printed edition: $z 9783030881832
856 4 0 $u https://doi.org/10.1007/978-3-030-88181-8
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)