The topology optimization design of complex products can significantly improve material and power savings, and reduce inertial forces and mechanical vibrations effectively. In this study, a large-tonnage hydraulic press was chosen as a typically complex product to present the optimization method. We propose a new reliability topology optimization method based on the reliability-and-optimization decoupled model and teaching–learning-based optimization (TLBO) algorithm. The supports formed by the plate structure are considered as topology optimization objects, characterized by light weight and stability. The reliability optimization under certain uncertainties and structural topology optimization are processed collaboratively. First, the uncertain parameters in the optimization problem are modified into deterministic parameters using the finite difference method. Then, the complex nesting of the uncertainty reliability analysis and topology optimization are decoupled. Finally, the decoupled model is solved using the TLBO algorithm, which is characterized by few parameters and a fast solution. The TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial stage and performing finer searches in the later stages. A numerical example of the hydraulic press base plate structure is presented to underline the effectiveness of the proposed method.

复杂产品的拓扑优化设计可以显着提高材料和功率的节省，并有效降低惯性力和机械振动。本研究选取大吨位油压机作为典型的复杂产品，提出优化方法。我们提出了一种基于可靠性和优化解耦模型和基于教学的优化（TLBO）算法的新的可靠性拓扑优化方法。由板结构形成的支撑被认为是拓扑优化对象，具有重量轻和稳定的特点。一定不确定性下的可靠性优化和结构拓扑优化协同处理。第一的，利用有限差分法将优化问题中的不确定参数修改为确定参数。然后，将不确定可靠性分析和拓扑优化的复杂嵌套解耦。最后，解耦模型采用参数少、求解速度快的TLBO算法求解。TLBO 算法通过自适应教学因子得到改进，以便在初始阶段加快收敛速度​​并在后期执行更精细的搜索。给出了液压机底板结构的数值示例，以强调所提出方法的有效性。其特点是参数少，求解速度快。TLBO 算法通过自适应教学因子得到改进，以便在初始阶段加快收敛速度​​并在后期执行更精细的搜索。给出了液压机底板结构的数值示例，以强调所提出方法的有效性。其特点是参数少，求解速度快。TLBO 算法通过自适应教学因子得到改进，以便在初始阶段加快收敛速度​​并在后期执行更精细的搜索。给出了液压机底板结构的数值示例，以强调所提出方法的有效性。