Meta-Action Unit-Based Modeling of Accuracy Stability, Error Propagation and Intelligent Compensation in CNC Machine Tools: A Comprehensive Review Toward Industry 4.0 and 5.0

The concept of Meta-Action Units (MAUs) has emerged as a promising paradigm for decomposing machine tool motion into fundamental action units, providing new insights into error propagation and accuracy stability in CNC machine tools. This paper presents a comprehensive review of accuracy stability from the MAU perspective. Fluctuation mechanisms induced by geometric errors, thermal effects, load-dependent deformations and wear-related degradation are systematically reviewed. Existing modeling, identification, and compensation methods are critically analyzed. A key contribution is the synthesis of a novel MAU-centric taxonomy integrating research on key MAU identification, precision remaining useful life prediction under incomplete maintenance, cascading fault propagation and reliability coupling mechanisms. The integration of screw theory, multi-body systems, and active learning Kriging is examined, along with hybrid approaches combining physics-based models with machine learning. The alignment of MAU-based digital twins with Industry 4.0 and Industry 5.0 is discussed. MAU decomposition provides a physically interpretable framework for accuracy formation. Hybrid Wiener–GPIM models achieve PRUL prediction errors below ten percent. Five research gaps are identified: uncertainty propagation, robust parameter identification, benchmark datasets, cost–benefit frameworks, and transfer learning. Addressing these gaps will guide the development of next-generation high-accuracy and intelligent CNC machine tools.