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

Pai Zheng, Yuanbin Wang, Xun Xu, Sheng Quan Xie

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1977-1990
Number of pages14
JournalInternational Journal of Advanced Manufacturing Technology
Issue number5-8
Publication statusPublished - 1 Jul 2017
Externally publishedYes


  • Additive manufacturing
  • Fuzzy axiomatic design
  • Multi-attribute decision making
  • Preference graph
  • Relative importance rating
  • Rough set

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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