Abstract
Spherical porous air bearing (SPAB) systems have been extensively used in various mechanical engineering applications. SPABs are promising materials in high-rotational speed, high-precision, and high-stiffness instruments. In SPAB systems, a rotor is supported by gas bearings, which provides higher rotational speed and lower heat generation environment than oil bearings do. Furthermore, SPAB does not cause deformation. Although, the supporting force of gas bearings is less, their stability is better than that of oil films. However, because the pressure distribution in the gas films is nonlinear, they are prone to failure at specific critical speeds, rotor imbalances, or inappropriate operations, which results in nonperiodic or chaotic motion and causes structural fatigue to the system. To understand and control the operating conditions of the SPAB systems during the nonperiodic motion, first, the governing equations of the SPAB system were solved to obtain the dynamic behavior of the rotor center. Then, the performance of the SPAB system were examined under different operating conditions by generating the maximum Lyapunov exponents (MLEs). However, the calculation process of MLE is extremely time consuming and complex. To solve this problem efficiently, a high-precision machine learning (ML)-based MLE prediction model was proposed in this study. The results show that the training process can be finished within few minutes, and the prediction process is able to be completed within few seconds. Meanwhile, the results demonstrate the merit of using the machine learning method for solving the MLE prediction problem and shorten the calculation time significantly. The proposed prediction model achieves excellent prediction outcome and it is more efficient and precise than traditional iteration scheme for the calculation of MLE. The feasibility of the proposed model is validated and the results also are the major contribution of this study.
Highlights
Spherical porous air bearing (SPAB) systems exhibit several attractive features such as low-noise rotation, high load capacity, improved damping properties, and zero friction
EXPERIMENTAL RESULTS This section presents the experimental results of the maximum Lyapunov exponents (MLEs) prediction models
The calculation of MLE is crucial in the design of the SPAB system
Summary
Spherical porous air bearing (SPAB) systems exhibit several attractive features such as low-noise rotation, high load capacity, improved damping properties, and zero friction. SPABs are simpler and cheaper than externally pressurized bearings. SPABs generate less heat and provide higher. The associate editor coordinating the review of this manuscript and approving it for publication was Yunhua Li. accuracy than oil bearings do. SPABs are applied in highspeed spindles and machine tools. In 1964, Sneck and Yen [1] considered a one-dimensional flow for radial direction in a porous medium and developed a perturbation solution for a finite journal bearing. They subsequently experimentally verified the theoretical solutions in 1965 [2]
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