Robust topology optimization considering load uncertainty based on a semi-analytical method

  • Yongfeng Zheng
  • Liang Gao
  • Mi Xiao
  • Hao Li
  • Zhen Luo
ORIGINAL ARTICLE
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Abstract

Uncertainty is omnipresent in engineering design and manufacturing. This paper dedicates to present a robust topology optimization (RTO) methodology for structural compliance minimization problems considering load uncertainty, which includes magnitude and direction uncertainty subjected to Gaussian distribution. To this end, comprehensible semi-analytical formulations are derived to fleetly calculate the statistical data of structural compliance, which is critical to the probability-based RTO problem. In order to avoid the influence of numerical units on evaluating the robust results, this paper considers a generic coefficient of variation (GCV) as robust index which contains both the expected compliance and standard variance. In addition, the accuracy and efficiency of semi-analytical formulas are validated by the Monte Carlo (MC) simulation; comparison results provide higher calculation efficiency over the MC-based optimization algorithms. Four numerical examples are provided via density-based approach to demonstrate the effectiveness and robustness of the proposed method.

Keywords

RTO Load uncertainty Semi-analytical method Expected compliance Standard variance 
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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • Yongfeng Zheng
    • 1
  • Liang Gao
    • 1
  • Mi Xiao
    • 1
  • Hao Li
    • 1
  • Zhen Luo
    • 2
  1. 1.State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.School of Mechanical and Mechatronic EngineeringThe University of TechnologyUltimoAustralia

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