Zheng, P, Wang, Y, Xu, X et al. (1 more author) (2017) A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes. International Journal of Advanced Manufacturing Technology, 91 (5-8). pp. 1977-1990. ISSN 0268-3768
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016, Springer-Verlag London. This is an author produced version of a paper published in International Journal of Advanced Manufacturing Technology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Rough set; Fuzzy axiomatic design; Preference graph; Multi-attribute decision making; Relative importance rating; Additive manufacturing |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Feb 2017 11:04 |
Last Modified: | 05 Jul 2018 13:44 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s00170-016-9890-8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:112313 |