超声键合换能系统非线性振动的实验研究
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摘要
本文利用激光多普勒测振仪的非接触和高精密特点,对超声键合系统的振动进行了精密测试,并用GP算法对该振动的时间序列进行了分析;计算了振动序列的关联维数的值,分析了外界影响关联维数的影响因素,得出了关于超声键合系统振动复杂性有意义的实验结果,对于换能系统的动力学建模的新途径进行了有意义的技术探索。主要工作包括:
     1.综述了对换能系统测试的基本方法,阐述了实验的基本原理与数据处理方法,并用扫频的方法对换能系统的机械导纳进行了测定,研究了劈刀的质量对整个换能系统的固有特性的影响。
     2.用相干函数的方法分析了轴向-俯仰、轴向-水平振动耦合的相关性。探讨了换能杆末端振动在无劈刀、劈刀对称安装和非对称安装时,俯仰寄生振动产生的根源。
     3.首先阐述了替代数据法的基本原理,并通过实例验证了算法的可靠性;采用替代数据法研究了换能系统振动实测时间序列的非线性特征。
     4.在实测数据的基础上,计算了换能杆空载时实际振动时输出端三个方向的关联维数值,定量地论证了振动时间序列的分形特征,同时分析了关联维数随采样频率、采样时间长度、噪声的影响,较全面地讨论了该方法使用的条件,为更进一步深入研究换能系统的非线性动力学模型奠定基础。
     本文为超声键合换能系统的非线性动力学模型的研究提供了新的思路。
Based on the high precision and non-touching advantages, the vibration of the ultrasonic bonding transforming system was tested and the complexity of the system was discussed. G-P algorithm was employed to analyse the nonlinear characteristic of the vibration time series. The correlated dimensions along the three different directions were calculated and the possible influencing factors of the values were analyzed. A number of valuable experimental results has been deduced which may be a reference for the modeling and optimizing of this dynamics system. This paper mainly included:
     1. The methods for testing the bonding system were summarized. All the mechanisms and related results of each method have been cited. Frequency sweeping method was employed to test the mechanical admittance with difference magnitude of bonding tool.
     2. Coherence Function was introduced to analyze the coherence characteristic along the axial-pitching and axial-horizontal direction and the source of the vibration along pitching direction in three different bonding tool fixing conditions was explored.
     3. Surrogate Data method was employed to identify the nonlinear characteristic of the vibration time series. The mechanism of this method was explained at first. Two typical examples were introduced to testify the stabilization of this method.
     4. Based on the experimental datas, correlated dimensions of the vibrating transducer were calculated. We have discussed the fractal characateristics of the time series quantitatively. The possible influencing factors such as sampling frequency, the datas length and noise, have been studied which maybe helpful for modeling of this complicated structure.
     The above research results can support for the modeling of this system.
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