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Machining accuracy retainability is considered to be one of the most important aspects in the process of performance evaluation and optimization design of the machine tools. Reliability based design optimization (RBDO) and robust design optimization (RDO) are the tools that search for safe structural systems with minimal variability of response when subjected to uncertainties in design parameters. The aim of this paper is to propose an approach that simultaneously considers reliability and robustness to allocate geometric accuracy of components for improving machining accuracy retainability under certain design requirements. Based on homogenous transformation matrices (HTMs), a comprehensive volumetric model explaining how individual errors in the components of a machine affect its volumetric accuracy (the coupling relationship) was established. By applying high-order moment standardization technique (HOMST), reliability and sensitivity with single failure mode were obtained and then the reliability model and the sensitivity model with multiple failure modes were developed and common methods such as Narrow bounds, AFOSM and Monte Carlo were used for verification and comparison. Meanwhile, a cost model taking manufacturing cost and present worth of expected quality loss into consideration was introduced. By taking the minimum possibility of failure and cost of machine tools as criterions and taking the reliability of machine tools as constraint of optimizing the basic parameters of machine tools, an approach to optimize the machining accuracy retainability of multi-axis NC machine tool based on robust design was developed and an example of improving the machining accuracy retainability for a five-axis NC machine tool was used to demonstrate the effectiveness of this approach. This study implies that it is possible to obtain the relationships between geometric errors and specify the accuracy grades of main feeding components of mechanical assemblies. [ABSTRACT FROM AUTHOR]

The components of machine tools are mainly fixed and connected by bolts. The performance of the assembly can be affected by the dynamic characteristics of the bolted joints. This paper presents a nonlinear virtual material method based on surface contact stress to describe the bolted joint for accurate dynamic performance analysis of the bolted assembly. Fractal geometry theory is used to describe the surface topography. The elastic modulus and shear modulus of one micro-contact are derived based on fractal contact theory. The equivalent elastic modulus, Poisson ratio, and density of the bolted joint can be obtained through the weighted mean method. In order to obtain the stress distribution, the contact surface is assumed flat in the macro-scale, and the uneven distribution of contact stress can be obtained by the finite element method (FEM). The contact surface can be divided into several sections, and the parameters of a virtual material layer can be determined based on the mean contact stress. Both theoretical and experimental results for a bolted joint are obtained for a box-shaped specimen under equal pre-tightening force and bending moment effect. The results show that the theoretical mode shapes are in good agreement with the experimental mode shapes. The relative errors between the theoretical and experimental natural frequencies are less than 4.41%, which indicates that the present nonlinear virtual material method is appropriate for the bolted joint in modeling CNC machine tools. [ABSTRACT FROM AUTHOR]

Abstract Reliable operating conditions of hydrostatic turntables are prerequisite to ensuring machine tool performance. The hydrostatic turntable is affected by multiple working conditions, therefore, methods for evaluating turntable load-carrying capacity has become research hotspot. In this paper, by analyzing bearing capacity, parameters closely related to the support performance of turntables are selected as recognition features and an artificial neural network (ANN) training method is proposed. The ANN method is based on numerical solutions of over-determined nonlinear equations (ODNE) to intelligently evaluate turntable performance. In this study, ANN and ODNE training are applied to evaluate the performance of hydrostatic turntables. Finally, to verify the feasibility of the method, an intelligent monitoring system is established to collect data on machine tools. Highlights • Factors influencing the performance of hydrostatic turntable were determined by analyzing the carrying ability of oil pads. • Based on the ANN structure for fast calculation of optimal weights, an ODNE training method was presented. • For intelligent monitoring of a hydrostatic turntable, BP and ODNE training are both recommended for training the ANN. [ABSTRACT FROM AUTHOR]