FCE-based health status evaluation and IGM-based failure prediction of tool magazine manipulator for CNC machine tools.

This article focuses on the health status evaluation and fault prediction of the tool magazine manipulator, a critical and failure-prone component of Computer Numerical Control (CNC) machine tools. It proposes a comprehensive evaluation method combining Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM) that integrates subjective Analytic Hierarchy Process (AHP) and objective Entropy Weight Method (EWM) to assess manipulator health more accurately. For fault prediction, an Improved Grey Model (IGM) enhanced by Particle Swarm Optimization (PSO) and a modified initial value parameter is developed to address small sample sizes and improve prediction accuracy over traditional grey models. Case studies demonstrate that the manipulator’s health status can be effectively classified, and the IGM provides reliable fault predictions, supporting proactive maintenance strategies to enhance machine tool reliability.