中国机械工程

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基于多目标遗传算法的贝塞尔超声变幅杆优化设计方法研究

纪华伟;赵双双;胡小平   

  1. 杭州电子科技大学,杭州,310018
  • 出版日期:2016-07-10 发布日期:2016-07-12
  • 基金资助:
    国家自然科学基金资助项目(51475130);国防科工局重大专项(A3920133001)

Optimal Design Method for Bezier Ultrasonic Horn Based on Multi-objective Genetic Algorithm

Ji Huawei;Zhao Shuangshuang;Hu Xiaoping   

  1. Hangzhou Dianzi University,Hangzhou,310018
  • Online:2016-07-10 Published:2016-07-12
  • Supported by:

摘要: 为设计满足蜂窝复合材料加工要求的高性能超声变幅杆,提出了一种基于多目标遗传算法的超声变幅杆优化设计方法。以变幅杆的结构参数为设计变量,以谐振频率和放大系数为优化设计目标,建立了贝塞尔超声变幅杆的数学优化模型。通过在遗传算法中调用ANSYS仿真软件,对变幅杆进行了建模和动力学分析,获得了计算目标函数所需的参数,采用多目标遗传算法求出了Pareto最优解集,在所求出的Pareto最优解集中选择了一组最符合设计要求的解作为超声变幅杆的设计参数。为验证设计的有效性,对所设计的变幅杆进行了性能测试并对蜂窝复合材料进行了试切实验。实验结果表明:通过该优化设计方法得到的变幅杆放大倍数为7.66,较优化设计前提高了29%,且工作频率更接近于设计频率。通过仿真分析和性能实验,验证了该方法的有效性和可靠性,试切实验结果表明所设计的变幅杆满足加工要求,工艺效果好。

关键词: 贝塞尔超声变幅杆, 多目标优化, 遗传算法, 蜂窝复合材料

Abstract: In order to design high performance ultrasonic horn that was used to manufacture honeycomb composite material, a new approach was put forward to design ultrasonic horn based on multi-objective genetic algorithm. By using structural parameters of the ultrasonic horn as design variables, the resonant frequency and amplification factor as optimization goals, an optimization mathematical model of the Bezier ultrasonic horn was established. To get the parameters for calculating fitness function, a finite model and dynamics analysis of ultrasonic horn were carried out by calling ANSYS software from genetic algorithms, and the Pareto-optimal solution set was obtained by genetic algorithm, then, the most suitable parameters of ultrasonic horn were chosen for horn design. At last, the performance test of ultrasonic horn and trial cut experiment of honeycomb composite material show that amplification factor of optimized ultrasonic horn is as 7.66, it is 29 percent higher, and the working frequency is closer to design frequency than un-optimized ultrasonic horn. Simulation and experimental results confirm the reliability and validity of the proposed design method.

Key words: Bezier ultrasonic horn, multi-objective optimization, genetic algorithm, honeycomb composite material

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