Fuzzy collaborative intelligence fuzzy analytic hierarchy process approach for selecting suitable three-dimensional printers

Abstract

Three-dimensional (3D) printing presents numerous opportunities for improving rapid prototyping and mass customization. However, the existing methods for assessing the performance of a 3D printer are associated with several problems. To solve these problems, a fuzzy collaborative intelligence fuzzy analytic hierarchy process (FAHP) approach is proposed in this study for assessing the performance of a 3D printer. In the proposed methodology, the alpha-cut operations method is first applied to derive the fuzzy priority of each criterion for each decision maker. Based on the derived priorities, the fuzzy-weighted average is then computed to assess the overall performance of each 3D printer for each decision maker. Thereafter, fuzzy intersection is applied to aggregate the assessment results of the decision makers. Finally, the center-of-gravity method is applied to defuzzify the aggregation result. By using the proposed methodology, the consensus among decision makers can be guaranteed. The proposed fuzzy collaborative intelligence FAHP approach has been applied to a real case of 10 3D printers. The experimental results indicated that the accuracy of the proposed methodology is superior to that of the existing methods, which yielded different ranking results owing to the use of approximation.