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In the state of machining service, to evaluate the machining accuracy reliability of multi-axis CNC machine tools, analyze the accuracy failure mode and the reliability sensitivity under the failure mode, this paper proposes a research method by the cross-correlation studies of geometric error parameters to improve and promote the accuracy reliability of CNC machine tools. Firstly, by multi-body system theory, the homogeneous coordinate transformation matrix between each body of the machine tool is established, and the spatial machining accuracy model is constructed. At the same time, considering the time series problem in the measurement process of geometric error parameters, this paper studies the correlation between the error parameters for reliability analysis and proposes a novel reliability analysis index for each measurement point in the machine tool workspace. To validation of the method, the analysis results of this paper are compared with those by Monte Carlo simulation. In addition, the accuracy failure conditions of the machine tool are studied, the failure state function of machine tool accuracy is established, and the accuracy failure modes that may occur are analyzed from this, to carry out the accuracy failure sensitivity analysis under the failure mode, and to identify key geometric errors which have a great impact on the machining accuracy reliability of machine tool in the failure mode. Finally, taking a 3-axis machine tool as an example, according to the analysis results, this paper puts forward measures to improve the accuracy reliability and verifies the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In actual engineering, material properties, load effects, and other factors of mechanical structures change due to long‐term use. In order to understand the operation of a mechanical structure in real time, it is crucial to obtain the dynamic trajectory of its reliability. Considering the time variability of a mechanical structure over time, uncertain random variables are introduced to express the uncertainty of various parameters of structures, and the Wiener process is used to describe the strength degradation process of structures so as to solve the calculation problem of time‐varying reliability of mechanical structures. Based on the advanced first‐order and second moment method (AFOSM), the proposed linearized Nataf change is used to complete the transformation from related nonnormal variables to independent standard normal variables in order to simplify the calculation process of reliability solution and solve the reliability calculation problem of random parameters subjected to arbitrary distribution. The deduced random variable sensitivity factor indicates the degree of influence of different random variables on the reliability of the mechanical structure, providing a theoretical basis for the optimal design and maintenance of a mechanical structure. The proposed method is analyzed using the cantilever beam and compared with the nonlinear Nataf transform and verified by the Monte Carlo simulation results. The results show that the proposed method can effectively solve the reliability sensitivity problem of structural system strength degradation. [ABSTRACT FROM AUTHOR]

The present study investigates the hybrid reliability modeling of structures in which the inputs contain both random variables and interval variables. Hybrid uncertainty is divided into three categories, including random variables mixed with random variables, interval variables mixed with interval variable, and random variables mixed with interval variables. In order to perform the reliability analysis of structural systems, first, the Bayes method is proposed in the present study to obtain distribution parameters of random variables. Moreover, the self‐sample method is introduced to obtain the interval boundaries based on the least available measuring data. Then, the reliability models are established for three situations and the reliability indices are defined and derived accordingly. The abovementioned three types of reliability indices outline the general situation of structural systems. Finally, the specific calculation process is described in details through different examples. Furthermore, the accuracy and efficiency of the proposed method is discussed by comparing the results obtained from the Monte Carlo simulation and those of other methods. The obtained results indicate that the performance of the proposed model in solving reliability modeling problems is better. [ABSTRACT FROM AUTHOR]