A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes

Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be e...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2017-07, Vol.91 (5-8), p.1977-1990
Main Authors: Zheng, Pai, Wang, Yuanbin, Xu, Xun, Xie, Sheng Quan
Format: Article
Language:English
Subjects:
CAE) and Design
Computer-Aided Engineering (CAD
Customization
Decision making
Design analysis
Engineering
Fuzzy sets
Industrial and Production Engineering
Markets
Materials selection
Mechanical Engineering
Media Management
Original Article
Performance evaluation
Process selection
Robustness
Three dimensional printing
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Summary:Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regard to two aspects, preference rating flexibility and performance evaluation objectivity . We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes. An example based on the previous research work of AM machine selection is given to validate its robustness for the priori articulation of AM process selection decision support.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-016-9890-8