Meta-action-oriented collaborative allocation optimization for accuracy-related key quality characteristics of CNC machine tools

Accuracy-related key quality characteristics (ARKQCs), namely accuracy, accuracy stability, and accuracy retainability, are important performance indicators to characterize the machining accuracy of machine tools. To achieve accurate allocation in the design stage, this paper reveals the coupling relationships among ARKQCs and proposes a meta-action-oriented collaborative allocation optimization method for ARKQCs of machine tools. Taking the function-motion-action decomposition tree as the allocation path, the error modeling mechanism of each layer is analyzed, and the error models of the F-M layer and M-A layer are established. Based on the error model, a quasi-Monte Carlo algorithm–based improved Sobol method is used to analyze the sensitivity of child layer error components to parent layer accuracy, and the accuracy stability model of machine tools is established. Taking the cost, reliability, and quality loss factors as objective functions, the ARKQC allocation optimization model is constructed, and NSGA-II algorithm is used to solve it optimally. To verify the effectiveness of the proposed method, a computer numerical control (CNC) gear milling machine is taken as a practical case, and a machining accuracy reliability evaluation method based on the quasi-Monte Carlo simulation is proposed to verify the rationality of the allocation results. This method provides theoretical guidance for comprehensively improving the accuracy design quality of CNC machine tools.