Integration of data science with product design towards data-driven design

被引：3

作者：

Liu, Ang ^{[1
]}

Lu, Stephen ^{[2
]}

Tao, Fei ^{[3
]}

Anwer, Nabil ^{[4
]}

机构：

[1] Univ New South Wales, Sydney, NSW 2052, Australia

[2] Univ Southern Calif, Los Angeles, CA 90089 USA

[3] Beihang Univ, Beijing 100191, Peoples R China

[4] Univ Paris Saclay, ENS Paris Saclay, LURPA, F-91190 Gif Sur Yvette, France

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2024年 / 73卷 / 02期

关键词：

Product design; Data science; Data-driven design; SUPPLY CHAIN MANAGEMENT; BIG DATA ANALYTICS; DATA QUALITY; PREDICTIVE ANALYTICS; ENGINEERING DESIGN; SENTIMENT ANALYSIS; SYSTEMS; FEATURES; MODEL; CHALLENGES;

D O I：

10.1016/j.cirp.2024.06.003

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper aims to investigate the scientific integration of data science with product design towards datadriven design (D3). Data science has potential to facilitate design decision-making through insight extraction, predictive analytics, and automatic decisions. A systematic scoping review is conduced to converge various D3 applications in four dimensions: the design dimension about design operations, the data dimension about popular data sources and common data-related challenges, the method dimension about the methodological foundations, and the social/ethical dimension about social/ethical considerations and implications. Based on the state-of-the-art, this paper also highlights potential future research avenues in this dynamic field. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

引用

页码：509 / 532

页数：24

共 287 条

[21] Personalized Dynamic Pricing with Machine Learning: High-Dimensional Features and Heterogeneous Elasticity [J].

Ban, Gah-Yi ;

Keskin, N. Bora .

MANAGEMENT SCIENCE, 2021, 67 (09) :5549-5568

[22] Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI [J].

Barredo Arrieta, Alejandro ;

Diaz-Rodriguez, Natalia ;

Del Ser, Javier ;

Bennetot, Adrien ;

Tabik, Siham ;

Barbado, Alberto ;

Garcia, Salvador ;

Gil-Lopez, Sergio ;

Molina, Daniel ;

Benjamins, Richard ;

Chatila, Raja ;

Herrera, Francisco .

INFORMATION FUSION, 2020, 58 :82-115

[23] Methodologies for Data Quality Assessment and Improvement [J].

Batini, Carlo ;

Cappiello, Cinzia ;

Francalanci, Chiara ;

Maurino, Andrea .

ACM COMPUTING SURVEYS, 2009, 41 (03)

[24] Engineering Privacy by Design: Are engineers ready to live up to the challenge? [J].

Bednar, Kathrin ;

Spiekermann, Sarah ;

Langheinrich, Marc .

INFORMATION SOCIETY, 2019, 35 (03) :122-142

[25]

Ben-Zvi D., 2018, International handbook of research in statistics education, P473, DOI DOI 10.1007/978-3-319-66195-7_16

[26]

Berger Katharina, 2023, Procedia CIRP, P354, DOI 10.1016/j.procir.2023.02.060

[27]

Bertoni A., 2020, Proceedings of the Design Society: Design Conference, V1, P101, DOI DOI 10.1017/DSD.2020.4

[28] Knowledge graph construction for product designs from large CAD model repositories [J].

Bharadwaj, Akshay G. ;

Starly, Binil .

ADVANCED ENGINEERING INFORMATICS, 2022, 53

[29]

Blake Roger H., 2011, Journal of Data and Information Quality, V2, P1, DOI [10.1145/1891879.1891881, DOI 10.1145/1891879.1891881]

[30]

Brahmi R., 2021, Proc. CIRP, V100, P259, DOI [10.1016/j.procir.2021.05.064, DOI 10.1016/J.PROCIR.2021.05.064]

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