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Dynamics Compensation and Rapid Resonance Identification in Ultrasonic-Vibration-Assisted Microforming System Using Magnetostrictive Actuator

机译:磁致伸缩致动器在超声振动辅助微成型系统中的动力学补偿和快速共振识别

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In this paper, a mechatronic system is developed to compensate for the hardware dynamics effect, and to achieve rapid resonance identification for an ultrasonic-vibration-assisted microforming system. Microforming has recently attracted great interests due to the need for miniaturized manufacturing systems in emerging applications. It has been demonstrated that significant benefits, such as the reduction of input energy and the prolongation of tool life, can be gained by introducing ultrasonic vibration into the microforming process, particularly when the vibration is maintained at the resonant frequency of the vibrating workpiece. However, the fundamental mechanism of ultrasonic vibration effect on the microforming process has not yet been understood; the electrical actuators currently used to generate the ultrasonic vibration are bulky and not suitable for miniaturization of the microforming system, and control of the ultrasonic vibration is primitive and far from being optimal. To tackle these challenges, a microforming platform based on a magnetostrictive actuator has been developed. The main contributions of this paper are two-fold: first, the use of a novel iterative learning control technique along with a vibration oscillation regulation circuit to compensate for the effect of the magnetostrictive actuator dynamics on the ultrasonic vibration generation, and thereby, maintain the same vibration amplitude across a large excitation frequency range; and secondly, the use of the Fibonacci search algorithm to achieve rapid online identification of the resonant frequency. Experimental results obtained on the developed magnetostrictive-actuator-based microforming system are presented and discussed to demonstrate the efficacy of the proposed approach.
机译:本文开发了一种机电一体化系统,以补偿硬件动力学效应,并实现超声振动辅助微成型系统的快速共振识别。由于在新兴应用中需要小型化的制造系统,因此微成型技术最近引起了极大的兴趣。已经证明,通过将超声振动引入到微成型过程中,尤其是当振动保持在振动工件的共振频率时,可以获得显着的好处,例如减少输入能量和延长工具寿命。但是,超声振动对微成型过程的影响的基本机理尚不清楚。当前用于产生超声振动的电致动器体积庞大并且不适合于微成型系统的小型化,并且对超声振动的控制是原始的并且远非最佳。为了解决这些挑战,已经开发了基于磁致伸缩致动器的微成型平台。本文的主要贡献有两个方面:首先,使用新颖的迭代学习控制技术以及振动振荡调节电路来补偿磁致伸缩执行器动力学对超声振动产生的影响,从而保持超声振动的产生。在较大的激励频率范围内具有相同的振幅;其次,使用斐波那契搜索算法来快速在线识别谐振频率。提出并讨论了在已开发的基于磁致伸缩致动器的微成形系统上获得的实验结果,以证明所提出方法的有效性。

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