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首页> 外文期刊>Mathematical Problems in Engineering >Effective Hybrid Algorithm of Taguchi Method, FEM, RSM, and Teaching Learning-Based Optimization for Multiobjective Optimization Design of a Compliant Rotary Positioning Stage for Nanoindentation Tester
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Effective Hybrid Algorithm of Taguchi Method, FEM, RSM, and Teaching Learning-Based Optimization for Multiobjective Optimization Design of a Compliant Rotary Positioning Stage for Nanoindentation Tester

机译：Taguchi方法，FEM，RSM和基于教学学习的优化有效混合算法，用于纳米压痕测试仪顺应旋转定位台的多目标优化设计

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摘要

This paper proposes an effective hybrid optimization algorithm for multiobjective optimization design of a compliant rotary positioning stage for indentation tester. The stage is created with respect to the Beetle's profile. To meet practical demands of the stage, the geometric parameters are optimized so as to find the best performances. In the present work, the Taguchi method is employed to lay out the number of numerical experiments. Subsequently, the finite element method is built to retrieve the numerical data. The mathematical models are then established based on the response surface method. Before conducting the optimization implementation, the weight factor of each response is calculated exactly. Based on the well-established models, the multiple performances are simultaneously optimized utilizing the teaching learning-based optimization. The results found that the weight factors of safety factor and displacement are 0.5995 (59.95%) and 0.4005 (40.05%), respectively. The results revealed that the optimal safety factor is about 1.558 and the optimal displacement is 2.096 mm. The validations are in good agreement with the predicted results. Sensitivity analysis is carried out to identify the effects of variables on the responses. Using the Wilcoxon's rank signed test and Friedman test, the effectiveness of the proposed hybrid approach is better than that of other evolutionary algorithms. It ensures a good effectiveness to solve a complex multiobjective optimization problem.
机译：提出了一种有效的混合优化算法，用于压痕测试仪顺应旋转定位台的多目标优化设计。舞台是根据甲虫的个人资料创建的。为了满足舞台的实际需求，对几何参数进行了优化，以找到最佳性能。在当前的工作中，采用Taguchi方法来布置数值实验的数量。随后，建立了有限元方法来检索数值数据。然后基于响应面法建立数学模型。在执行优化实施之前，必须准确计算每个响应的权重因子。基于良好建立的模型，利用基于教学学习的优化来同时优化多种性能。结果发现，安全系数和位移的权重因子分别为0.5995（59.95％）和0.4005（40.05％）。结果表明，最佳安全系数约为1.558，最佳位移为2.096 mm。验证与预测结果非常吻合。进行敏感性分析以识别变量对响应的影响。使用Wilcoxon的秩符号检验和Friedman检验，提出的混合方法的有效性优于其他进化算法。它确保解决复杂的多目标优化问题具有良好的效果。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第2期|4191924.1-4191924.16|共16页
  • 作者

    Minh Phung Dang; Thanh-Phong Dao; Ngoc Le Chau; Hieu Giang Le;

  • 作者单位

    Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam;

    Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam|Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam;

    Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam;

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