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Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. [ABSTRACT FROM AUTHOR]

Working accuracy and service life of large structural parts such as lathe beds, columns, etc. of a heavy-duty machine tool with large self-weight is directly influenced by the foundation-soil system. Therefore, simulation of the interaction of the heavy-duty machine tool and its foundation-soil system is a key technological requirement from a manufacturing perspective. However, owing to the size of the structural parts of most heavy-duty machine tools, and the complex and oversized structure of their foundation-soil system, typical simulation environments fail to meet the computational requirements. With an aim to carry out simulations and meet the computational demand for the interaction of heavy-duty machine tools and their foundation-soil system; a constitutive model with a transversely isotropic composite material (the reinforced concrete foundation) was developed to reduce computation load. Then, based on fractal theory, equations for calculating stiffness of the joint surface and damping matrix were deduced to overcome common technical difficulty facing the heavy-duty machine tool industry. Meanwhile, establishing a finite element model (FEM) framework in a cloud-computing environment, rapid solution of the FE model for heavy-duty machine tool foundation-soil system interactions was realized. Finally, a comparison of experimental results can be used to validate accuracy of the analysis. Furthermore, cloud-computing simulation of a heavy-duty machine tool foundation-soil system was used to evaluate efficacy of different types of isolation trenches. The results provide a basis for the construction of the foundations of a heavy-duty machine tool and the selection of an optimisation scheme. [ABSTRACT FROM AUTHOR]

Hydrostatic systems are considered as essential supporting structures in heavy machine tools. The calculations and analyses for hydrostatic bearings are always laborious because of the involvement of several disciplines such as elastic mechanics, hydromechanics, thermodynamics, and other factors in the design. It is well known that large data and cloud technology are capable of processing and transmission of hydrostatic system studies. In this work, a cloud manufacture model is presented to provide big data storage, transmission, and processing platform for designers, manufacturers, and users of hydrostatic bearings. All participants involved in the manufacturing were linked together by timely information communication in order to ensure that the customized products met with the actual conditions in the cloud server. Based on the actual requirements of the user, the carrying capacity of hydrostatic system was analyzed by a designer using finite difference method and the results were sent to the manufacturer for machining of components. The monitored data from the consumer could be fed back to the designer for performance evaluation through the cloud server. It was found that customized manufacturing tasks could be finished more reliably and efficiently if all participants exchanged the data through cloud platform in real time. [ABSTRACT FROM AUTHOR]

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]

Geometric errors directly affect the tool tip position, reduce machining accuracy, and are one of the most important errors of multi-axis machining tool. However, the geometric errors are intercoupling, and the measured values at different points vary and are stochastic. The identification of the most crucial geometric errors and the determination of a method to control them is a key problem to improve the machining accuracy of machine tool. To achieve this goal, a new analytical method, to identify crucial geometric errors for a multi-axis machine tool is proposed here based on multibody system (MBS) theory and global sensitivity analysis. The volumetric error modeling of multi-axis machine tool has been given by MBS theory, which describes the topological structure of multibody system simply and conveniently in a matrix. The stochastic characteristic of geometric errors is taken into consideration and Sobol global sensitivity analysis method is introduced to identify crucial geometric errors of machine tool. A vertical machining center is selected as an illustration example. The analysis results reveal that the analytical method presented in this paper can identify the crucial geometric errors and are helpful to improve the machining accuracy of multi-axis machine tool. [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]