The Application of Magnetic Bearings in High-Speed Spindles

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Magnetic bearings are increasingly being used in high-speed spindles of machines, allowing rotational speeds of up to 100,000 rpm. This technology offers significant advantages over traditional bearings, such as improved surface finish and a much longer service life. Although still in the experimental phase, magnetic bearings have already demonstrated many benefits. They are not affected by thermal effects, do not suffer from mechanical wear, and can automatically shut down in case of overload. Traditional bearings provide radial or axial support for the main shaft, allowing relative movement between the inner and outer rings. These bearings typically use balls or rollers to transmit the supporting force. Oil-film bearings, on the other hand, use pressurized oil for lubrication and cooling. Both types have been widely used for many years, but they come with limitations in terms of speed and precision. In contrast, magnetic bearings operate without physical contact. They use electromagnetic forces to suspend the rotating components, eliminating friction, wear, and vibration. This results in higher stability and the ability to reach extremely high speeds. The electromagnetic force is controlled by adjusting the current based on feedback from sensors that monitor the gap between the rotor and stator. A typical magnetic bearing system consists of three main components: - **Bearings and Sensors**: The bearings provide the necessary support force to the spindle, while the sensors collect real-time position data. - **Control System**: This system processes the sensor input and adjusts the current to generate the appropriate corrective force, ensuring stable operation. - **Power Supply and Signal Processing Unit**: It supplies the required power and handles signal processing for accurate control. Despite their advantages, magnetic bearings also have some disadvantages. They tend to be larger in size, require a continuous power supply, and involve complex control algorithms. However, ongoing research and development are addressing these challenges.

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