Data-driven product design toward intelligent manufacturing: A review

With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design...

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Veröffentlicht in:International journal of advanced robotic systems Jg. 17; H. 2; S. 172988142091125
Hauptverfasser: Feng, Yixiong, Zhao, Yuliang, Zheng, Hao, Li, Zhiwu, Tan, Jianrong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.03.2020
Sage Publications Ltd
SAGE Publishing
Schlagworte:
Algorithms
CAD
Computer aided design
Computer models
Computer simulation
Customers
Data mining
Data warehouses
Decision making
Design analysis
Designers
Intelligent manufacturing systems
Iterative methods
Knowledge based engineering
Mapping
Product design
Product life cycle
Programming languages
Solution space
conceptual design
knowledge base
detailed design
data modeling
requirement analysis
Data-driven design
ISSN:1729-8806, 1729-8814, 1729-8814
Online-Zugang:Volltext
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Abstract With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.
AbstractList With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.
Author Zhao, Yuliang
Tan, Jianrong
Zheng, Hao
Feng, Yixiong
Li, Zhiwu
Author_xml – sequence: 1
  givenname: Yixiong
  surname: Feng
  fullname: Feng, Yixiong
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  givenname: Yuliang
  surname: Zhao
  fullname: Zhao, Yuliang
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  givenname: Hao
  orcidid: 0000-0003-3259-9153
  surname: Zheng
  fullname: Zheng, Hao
  email: haozheng@zju.edu.cn
– sequence: 4
  givenname: Zhiwu
  surname: Li
  fullname: Li, Zhiwu
– sequence: 5
  givenname: Jianrong
  surname: Tan
  fullname: Tan, Jianrong
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Issue 2
Keywords conceptual design
knowledge base
detailed design
data modeling
requirement analysis
Data-driven design
Language English
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Snippet With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of...
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SubjectTerms Algorithms
CAD
Computer aided design
Computer models
Computer simulation
Customers
Data mining
Data warehouses
Decision making
Design analysis
Designers
Intelligent manufacturing systems
Iterative methods
Knowledge based engineering
Mapping
Product design
Product life cycle
Programming languages
Solution space
Title Data-driven product design toward intelligent manufacturing: A review
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https://doaj.org/article/11800add9b3c4238925905d9f911a7c8
Volume 17
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