Identification of maintenance significant items for machine tools by integrating DEMATEL and MABAC with spherical fuzzy sets

TL;DR: This study proposes a novel MSI identification method integrating DEMATEL and MABAC with spherical fuzzy sets to evaluate criteria weights and rank maintenance significant items for machine tools, demonstrating its effectiveness and robustness in a CNC lathe application.

Abstract: The identification of maintenance significant items (MSIs), which is a multi-criteria decision-making (MCDM) process, is the primary phase of reliability centered maintenance. The identification process is typically vague and uncertain as a result of the structural complexity and functional diversity of machine tools. Therefore, there are limited effective methods for identifying MSIs in machine tools. In this study, we propose a novel MSI identification method that integrates two MCDM methods, namely the decision-making trial and evaluation laboratory (DEMATEL) and multi-attributive border approximation area comparison (MABAC). A novel extension of the fuzzy theory called the spherical fuzzy (SF) set concept is combined with the integrated DEMATEL-MABAC method. SF-DEMATEL is used to evaluate the criteria weights of machine tools. Then, based on the judgement information regarding alternatives under each criterion, SF-MABAC is used to identify and rank MSIs. The proposed integration method was applied to a CNC lathe with six criteria and 11 alternatives. Robustness analysis was conducted to verify the stability and effectiveness of the proposed method. The results demonstrate that the integration of DEMATEL and MABAC under the SF concept is a reasonable and applicable method for MSI identification. The results support engineers into reasonably identifying the significant maintenance items for machine tools.