An improved FMECA method for CNC machine tools based on combined-variable weight and cloud model

As core equipment in modern manufacturing, the reliability of computer numerical control (CNC) machine tools directly impacts factory production efficiency and product quality. Failure mode, effects and criticality analysis (FMECA) is a commonly employed method for reliability analysis. However, the traditional FMECA method suffers from issues such as one-sided consideration of risk factors, equal weighting of risk factors, and high subjectivity in expert scoring. To address the first issue, this study incorporates maintainability (M) as a new risk factor, in addition to the traditional risk factors of severity (S), occurrence (O), and detectability (D). To tackle the second issue, this study adopts a combined weighting approach that integrates subjective weights obtained through the analytic hierarchy process (AHP) with objective weights derived from the maximizing deviation method (MDM). Subsequently, the weights of each risk factor are determined based on variable weight theory. To mitigate the third issue, this study utilizes the cloud model to score each risk factor, thereby reducing scoring subjectivity. Finally, this study employs the technique for order preference by similarity to ideal solution (TOPSIS) method to calculate the risk priority number (RPN) of failure modes and rank the criticality of failure modes and subsystems. Through a case study on a certain type of CNC machine tool and a comparison of the results with those obtained using the traditional FMECA method, the rationality of the proposed approach is validated.