Reliability analysis of machine tool assembly using an improved FMECA method based on fuzzy logic and CBWM

As the core of the equipment manufacturing industry, the development level of CNC machine tools is closely linked to national strategic objectives, making the enhancement of scientific methodologies for reliability assessment during the assembly stage particularly important. This study proposes an improved Failure Mode, Effects, and Criticality Analysis (FMECA) method that integrates fuzzy logic with the Cognitive Best-Worst Method (CBWM). The approach addresses the strong subjectivity inherent in traditional FMECA scoring by establishing a trapezoidal fuzzy scoring system. Concurrently, CBWM is employed to optimize scoring weights, replacing the conventional equal-weighting assumption, thereby enabling the construction of a more rational risk priority number (RPN) calculation model. On this basis, a multi-dimensional extension analysis of the assessment results is conducted using the Reliability, Availability, Maintainability, and Safety (RAMS) framework. The proposed method is applied to the assembly of the G60D heavy-duty CNC machine tool as a case study, where fuzzy modeling of typical failure modes and improved RPN ranking are performed. The findings demonstrate that the method effectively identifies high-risk processes and enhances the accuracy and stability of risk detection. The research outcomes hold substantial practical value for assembly reliability diagnosis and process optimization in complex equipment systems.