Xiaolei Deng

This paper proposes a method to analyze motion errors in a five-degree-of-freedom hydrostatic turntable with internal feedback under eccentric load. The motion error models of thrust and journal bearings are derived separately, revealing the mechanism of the influence of manufacturing errors of thrust plate and shaft on motion errors. The results demonstrate that the hydrostatic oil film exhibits an error averaging effect. When the amplitude of the mating surface error reaches 15 μm, the corresponding linear deviation of the turntable remains below 0.3 μm, indicating that the oil film can effectively suppress the transmission of manufacturing errors. However, the pressure oil film cannot completely balance the errors on the film binding surface, especially when the amplitude of the binding surface error is larger, resulting in a weaker ability of the oil film to balance.

Aiming at improving the structural performance of the high-precision turning-milling combined machine tool bed, a multi-objective collaborative optimization framework that integrates bionic design principles and the Taguchi method is developed in this study. This framework first extracts the static and dynamic characteristics of the bed structure through finite element simulation. Then, inspired by the honeycomb configuration as a bionic prototype, it innovatively redesigns the internal rib layout. Subsequently, systematic optimization of key dimensional parameters is conducted via Taguchi orthogonal experiments, ultimately establishing a mathematical model with the comprehensive objectives of mass reduction, deformation suppression, and natural frequency enhancement. The results show that the quality of the optimized bed is reduced by 6.7%, the maximum deformation is decreased by 6.15%, and the fourth-order natural frequency is increased by 2.8%. It achieves lightweighting while significantly enhancing the static and dynamic performance of the structure, providing a feasible path for the green design and performance improvement of high-precision machine tools.

One of the key elements influencing machining precision is the thermal inaccuracy of the CNC machine tool feeding mechanism. To improve prediction capability, this article presents a Transformer-GRU composite model for thermal error prediction, which adopts an information fusion method to consider the combined effects of multiple factors on thermal error. First, an experimental platform is constructed to measure thermal error, temperature and vibration data in the feeding system. Fuzzy C-means and Grey Relational Analysis are adopted to screen out screen out the data of temperature sensitive points, and vibration data are processed by means of Empirical Mode Decomposition with Wavelet Thresholding, Spearman Correlation Coefficient and Kernel Principal Component Analysis. Then, the temperature and vibration data are fused using multi-source information fusion technique. A Transformer-GRU composite model that embeds the Gated Recurrent Unit (GRU) into Transformer architecture is introduced. Finally, the temperature, vibration and displacement data are fused to construct a composite Transformer-GRU model for thermal error prediction of the feeding system. To prove the effectiveness of the presented model, its prediction results are compared and analyzed with Transformer and Transformer-LSTM models. The performance metrics show that the RMSE, MAE, and MSE of the Transformer-GRU composite model are 1.40, 1.15, and 1.97, respectively, which are 36.9%, 15.4%, and 29.9% lower than those of the Transformer's 1.68, 1.36, and 2.81, respectively, and those of the Transformer-LSTM's 2.22, 2.89, 4.91 reduced by 36.9%, 39.1%, 59.9%, respectively. The results demonstrate that the Transformer-GRU composite model, which utilizes multi-source information fusion, achieves higher prediction accuracy.

As a high-precision machining equipment, the turning-milling machine tool requires its bed structure the primary load-bearing component to exhibit excellent dynamic and static characteristics that significantly influence machining accuracy and efficiency. To ensure machining precision, this paper proposes a multi-objective collaborative optimization design methodology integrating Finite Element Analysis(FEA) and Taguchi Method. This approach employs FEA technology to obtain the static and dynamic characteristics of the machine tool bed(MTB) structure, followed by multi-objective collaborative optimization using Taguchi Method to achieve comprehensive performance enhancement. Compared with the traditional research methods for optimizing machine tool beds, the FEA technology can handle more complex geometric shapes and boundary conditions, improve the accuracy and reliability of data, and enhance the optimization efficiency through the Taguchi method, achieving multi-objective joint optimization. Comparative analysis demonstrates that the optimized structure achieves 5.14% reduction in maximum deformation, 1.75% decrease in mass, and 1.04% improvement in fourth-order natural frequency. The results validate the effectiveness of this design methodology in achieving efficient MTB optimization for turning-milling machine tools, simultaneously enhancing dynamic-static performance while realizing lightweight design. This research provides valuable references for advancing precision improvement and green manufacturing research in turning-milling machine tools.

To comprehensively improve the thermal, static and dynamic characteristics and achieve lightweighting for CNC machine tools, this paper proposes a multi-objective joint optimization method based on coupled thermal–mechanical–vibration collaborative analysis. The dual-spindle component of a CNC machine tool is taken as the parameterized model. According to the theories of thermal characteristics, statics, and dynamics, the solution of thermal-mechanical coupling deformation and the solution of vibration characteristics under prestress are repeatedly conducted, that are working collaboratively with each process of parameters sensitivity computing, selection of design variables, central composite design, and multi-objective joint optimization. The response surfaces of the objective functions are established. The optimal parameter combination for improving CNC machine tool performance is effectively obtained. And the multiple objectives of improving the thermal, static and dynamic characteristics, as well as lightweighting, are achieved. The results show that the mass of the optimized component is reduced by 10.1%; the first-order natural frequency is increased by 3.9%; the coupling deformation of the end face of the left spindle seat is reduced by 5.3%; and the coupling deformation of the end face of the right left spindle seat is reduced by 9.0%, while the temperature of the component hardly increases. This indicates that this method can comprehensively improve the performance of CNC machine tool components and provide a reference for the multi-objective joint optimization design of CNC machine tools.

As the core component of electronic equipment, vibration load has an important effect on the reliability of circuit boards. However, construction machinery has high requirements for construction efficiency and safety performance, and also has high maintenance costs. In this paper, a vibration reliability analysis and structural optimization of the control circuit board of a drilling rig are conducted. First, Failure Mode, Effects and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) qualitative and quantitative analysis were used to find the weak links in the circuit board. The quantitative and qualitative analysis results of FMECA and FTA show that the critical probability importance values of the chip inductor and TVS diode short circuit are the highest, at 0.249 and 0.173, respectively. They are up to 0.239 higher than those of the other components. Then, according to the analysis results, a precise simplified model is established for the core components, including the weak links, and modal simulations and modal tests are carried out. The key influencing parameters are obtained through comparative analysis and research on natural frequency and frequency response curves. Finally, the position of the stress–strain sensitive source in the real working environment is determined by a dynamic simulation analysis of the random vibration performance of the finite element model. The results can provide an optimization basis for an anti-vibration design of circuit boards.

To address the limitations in predictive capabilities of thermal error models built from single-source, single-structure data, this paper proposes a thermal error prediction model based on decision-level fusion of multi-source heterogeneous information to enhance prediction accuracy. First, an experimental platform for multi-source heterogeneous information acquisition was constructed to collect thermal error data from different signal sources (multi-source) and different structures (heterogeneous). Next, based on the characteristics of the multi-source and heterogeneous data, relevant features were extracted to construct the feature set. Then, using the feature information set of the multi-source and heterogeneous data, thermal error prediction sub-models were established using Nonlinear Autoregressive models with exogenous inputs (NARX) and Gated Recurrent Units (GRUs) for a vertical machining center spindle. Finally, the entropy weight method was employed to assign the weights for the linear-weighted fusion rule, achieving decision-level fusion of multi-source heterogeneous information to obtain the final prediction result. This result was then compared with experimental results and the prediction results of single-source models. The findings indicate that the proposed thermal error prediction model closely matches the actual results and outperforms the single-source and single-structure data models in terms of Root-Mean-Square Error (RMSE), Coefficient of Determination (R²), and Mean Absolute Error (MAE).