New XAI tools for selecting suitable 3D printing facilities in ubiquitous manufacturing

Yu Cheng Wang, Toly Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Several artificial intelligence (AI) technologies have been applied to assist in the selection of suitable three-dimensional (3D) printing facilities in ubiquitous manufacturing (UM). However, AI applications in this field may not be easily understood or communicated with, especially for decision-makers without relevant background knowledge, hindering the widespread acceptance of such applications. Explainable AI (XAI) has been proposed to address this problem. This study first reviews existing XAI techniques to explain AI applications in selecting suitable 3D printing facilities in UM. This study addresses the deficiencies of existing XAI applications by proposing four new XAI techniques: (1) a gradient bar chart with baseline, (2) a group gradient bar chart, (3) a manually adjustable gradient bar chart, and (4) a bidirectional scatterplot. The proposed methodology was applied to a case in the literature to demonstrate its effectiveness. The bidirectional scatterplot results from the experiment demonstrated the suitability of the 3D printing facilities in terms of their proximity. Furthermore, manually adjustable gradient bars increased the effectiveness of the AI application by decision-makers subjectively adjusting the derived weights. Furthermore, only the proposed methodology fulfilled most requirements for an effective XAI tool in this AI application.

Original languageEnglish
Pages (from-to)6813-6829
Number of pages17
JournalComplex and Intelligent Systems
Issue number6
StatePublished - Dec 2023


  • Alpha-cut operations
  • Explainable artificial intelligence
  • Fuzzy technique for order preference by similarity to ideal solution
  • Ubiquitous manufacturing


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