Non-contact R-test is used for the dynamic measurement of five-axis machine tools. However, due to the spherical curvature, calculating the spherical center displacement (SCD) by using the non-contact R-test is challenging and inefficient. Thus, in this paper, an efficient calculation method based on multiple Gaussian process regression (GPR) is proposed to calculate the SCDs of the continuous trajectory by using the non-contact R-test. In the proposed method, first, the transformation matrix is constructed to roughly calculate the SCD. Second, the measurement error caused by the spherical curvature is solved and compensated by using the GPR model. Next, the threshold is set to remove some of the set points and reconstruct GPR to improve the model accuracy. Finally, the transformation matrix and the GPR model are outputted to calculate the SCDs of the continuous trajectory. We constructed a micro S trajectory to verify the proposed method. Experimental results showed that compared with the neural network method, the proposed method achieves higher model accuracy with shorter model construction time and improves the calculation efficiency by 99%.
Strong shear force developed in the lubrication film in the high-speed spindles will resulting high temperature rise and expanding the mandrel, which would arouse safe servicing problem. This study aimed to address the temperature rise issue in spindles through the use of substitutes in the spindle bearing material. To this end, we developed a spindle with a water-lubricated ceramic hydrodynamic and hydrostatic journal bearing. To support the selection of ceramic materials for the water-lubricated journal bearing, we initially performed application-oriented tests; to this end, the water absorption and porosity among alumina, silicon nitride, and silicon carbide were considered. To investigate the performance, and particularly the temperature characteristics of the hybrid bearing, a water-lubricated ceramic hydrodynamic and hydrostatic bearing test rig was designed to include temperature sensors. At the test rig, the radial clearance was measured for solving the temperature distribution of the bearing during the simulation. The experimental results helped verify that the water temperature increased by approximately 0.7°C as the rotation speed increased from 1200 rpm to 5400 rpm without an external radial load. The increase in temperature with and without radial load under a rotation speed of 3600 rpm was compared, and the results revealed a difference of 0.14°C. This indicates that the developed ceramic bearing improves the thermal stability of the spindle which shows its potential for industrial application in machine tools or other high rotation speed machines.
Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.
Under high-speed and high-acceleration conditions, flexibility outside the control loop (FOCL) is one of the critical incentives for trajectory errors (TEs). Because the FOCL will deform and vibrate under the action of inertial forces, which leads to TEs. FOCL contains multiple components, and the contribution of each component to TE needs to be evaluated. This paper first proposes a frequency domain dynamic model-based multi-axis machine tool trajectory tracking simulation method and a FOCL-induced TE decomposition method. Then using the proposed methods, quantitatively evaluates the contribution of different components of FOCL to TE caused by FOCL and discusses the dominant influencing factors. The mechatronics model of the machine tool is established in the frequency domain. This allows the lead screw to be equivalent to the spectral beam element, which is generated using the exact solution of the governing differential equations and can describe the lead screw’s higher-order dynamics with fewer degrees of freedom. The TE of the test trajectory is calculated using a setpoint-trajectory-based numerical method. Using this method, the calculated TEs have the same reference and can be directly subtracted to achieve TE separation. Hence, the decomposition of TE caused by FOCL can be achieved by further calculating the TE of the tool center point (TCP) trajectory that only considers part of the FOCL components and subtracting it from the TE of the feedback trajectory.
Variational Mode Decomposition (VMD) provides a robust and feasible scheme for the analysis of mechanical non-stationary signals based on the variational principle, but this method still has no adaptability, which greatly limits the application of this method in bearing fault diagnosis. To solve this problem effectively, this paper proposes a novel fluctuation entropy (FE) guided-VMD method based on the essential characteristics of fault impulse signals. The FE reported in this paper not only considers the order of amplitude values but also considers the variation of amplitude, and hence it can comprehensively characterize the transient and fluctuation characteristics of rolling bearing fault impulse signal. On the basis of establishing FE, the FE-based fitness functions are then conducted, after which the mode number and balance parameter can be adaptively determined. Meanwhile, an adaptive neighborhood statistical model is developed to further reduce the noise of the mode component containing fault information so as to highlight the periodic impulse component more significantly and improve the diagnostic accuracy. Simulation and case analysis show that this research is effective and quite accurate in fault mode separation and fault feature enhancement. Compared with the traditional VMD method and the current common diagnosis methods, the proposed method has obvious advantages in the comprehensive utilization of fault impulse information and enhanced diagnosis.
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