This paper focuses on the weight reduction optimization of a forearm of a bucket-wheel stacker reclaimer considering uncertainties of structural parameters, material properties, loads, and surrogate model. However, the optimization problem is a high-dimensional problem, with dozens of independent variables, which has negative effects on the optimization efficiency. Considering that millions of iterations are required for the reliability-based optimization, the finite element model can cause overwhelming computational cost. In addition, due to its complex structure and working conditions, multiple uncertainties exist in practical applications and affect the reliability of a design, especially the uncertainty of the surrogate model. To address these challenges, the sensitivity analysis is performed to improve the optimization efficiency by selecting main factors. The Kriging model with high accuracy is constructed to reduce the computational cost. In order to improve the optimization efficiency further, the deterministic optimization is performed firstly, and the optimal design is used as the initial point of the reliability-based optimization algorithm. For estimating the reliability, the multiple uncertainty models are constructed. Finally, according to the design requirements and taking the multiple uncertainties into account, the reliability-based optimization is proposed and carried out. The result proves that the weight is reduced greatly and the reliability is kept at a high level.

考虑结构参数，材料特性，载荷和替代模型的不确定性，本文着重于斗轮堆取料机前臂的轻量化优化。然而，优化问题是一个高维问题，具有数十个自变量，对优化效率产生负面影响。考虑到基于可靠性的优化需要数百万次迭代，因此有限元模型可能会导致压倒性的计算成本。另外，由于其复杂的结构和工作条件，实际应用中存在多种不确定性，并影响设计的可靠性，尤其是替代模型的不确定性。为了应对这些挑战，通过选择主要因素进行敏感性分析，以提高优化效率。构建具有高精度的克里格模型以减少计算成本。为了进一步提高优化效率，首先进行确定性优化，并将优化设计作为基于可靠性的优化算法的起点。为了估计可靠性，构建了多个不确定性模型。最后，根据设计要求并考虑多种不确定性，提出并进行了基于可靠性的优化。结果证明，重量大大减轻，可靠性保持在较高水平。为了进一步提高优化效率，首先进行确定性优化，并将优化设计作为基于可靠性的优化算法的起点。为了估计可靠性，构建了多个不确定性模型。最后，根据设计要求并考虑多种不确定性，提出并进行了基于可靠性的优化。结果证明，重量大大减轻，可靠性保持在较高水平。为了进一步提高优化效率，首先进行确定性优化，并将优化设计作为基于可靠性的优化算法的起点。为了估计可靠性，构建了多个不确定性模型。最后，根据设计要求并考虑多种不确定性，提出并进行了基于可靠性的优化。结果证明，重量大大减轻，可靠性保持在较高水平。根据设计要求并考虑多种不确定性，提出并进行了基于可靠性的优化。结果证明，重量大大减轻，可靠性保持在较高水平。根据设计要求并考虑多种不确定性，提出并进行了基于可靠性的优化。结果证明，重量大大减轻，可靠性保持在较高水平。