Mechanical code lock is a kind of safety device used for special occasions. It has the characteristics of long service life, high reliability and low error decoding rate. It is used in banks, weapons, ammunition stores and other occasions. In the past, mechanical locks were mainly manufactured using precision machining methods. With the rapid development of semiconductor manufacturing processes and ultra-precision processing technologies, the use of MEMS technology to miniaturize coded locks has become an inevitable development. As the size of the structure becomes smaller and smaller, the accuracy requirements for the micro-motor of the driving mechanism of the forging wheel also increase.
The MEMS coded locks discussed in this article have two identical and parallel mounted ultramicromotors, each equipped with its own identifying wheel. For the stator winding diagram and rotor pole distribution diagram of the AMD motor, it uses the same size of stator and rotor, and the novel structure is distributed up and down. As long as the rotor magnetic pole acts on the direction of the force on the stator coil, and the commutation occurs at the right moment, the rotor rotates. In order to use the structure of the code lock for the drive using the ultra-micro motor, the ultra-micro motor steps through the shaft and rotates the code wheel with the code tooth. If the password is correct, the two codewheels on the two code wheels step with the stepping of the two coaxial micromotors, the teeth are not meshed, the lock is opened; if the password is wrong, it will trigger two The verification teeth on the code wheel compete and the lock is stuck. Therefore, when the lock is unlocked, the two groups of ultra-micro motors with parallel mounting wheels can accurately and accurately step to the specified position respectively. That is, whether or not the stepping of the ultra-fine motor is stable and reliable is the key to the combination lock. important. This article discusses the 6.9mm diameter electro-magnetic supermicromotor developed by the quasi-LIGA process as the core driving component to drive the ultra-micromotor stepper control when the codec wheel is decoded.
The coded lock structure indicates the torque analysis of the ultra-micro motor The electromagnetic torque generated by the super-micro motor is very small, generally in the order of LNm. However, the proportion of the loss due to the friction of the ultra-micro motor tends to be large, and the conventional lubrication method cannot be used in the ultra-micro motor. Therefore, under the load, the micro-motor can generate enough torque to determine whether the micro-motor can step.
In the micro-code lock system, according to the decoding requirements, the two forging wheels must complete one cycle in 16 steps, so the super-micro-motor stepping requires each step to go 22.5b. In the specific implementation, the use of two to three The way of electricity. The following will analyze the motor torque in the two-three-phase power.
From the vector diagram, the torque when the two phases are energized is: MEMs cipher lock driven electromagnetic stepper motor control study, prediction method 2 is the fitting function method "1.17. 86%. The results show that the silicon steel sheet The neural network prediction model can be seen from Table 1, its maximum prediction error is 4.63% and high accuracy, and the prediction error is generally within 5%.
Table 1 DW310-35 cold-rolled silicon steel sheet material property prediction results (/ = 2000Hz) measured prediction method 1 prediction method 2 error / 4 Conclusion Harmonic analysis method for high-speed inverter motor simulation, the non-sinusoidal excitation solution to the traditional problem The sinusoidal excitation solves the problem and solves the problem that the silicon steel sheet material data is difficult to obtain under non-sinusoidal excitation.
The first application of three-layer forward neural network to predict the properties of silicon steel sheet material can use the generalization ability of neural network to predict the magnetization curve and loss of silicon steel sheet under sinusoidal excitation at different frequencies based on the limited data provided by silicon steel sheet manufacturers. curve.
The examples show that the prediction accuracy of the neural network prediction model is higher than the fitting function method and is an effective and feasible prediction model.
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