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A probabilistic approach for optimising hydroformed structures using local surrogate models to control failures

机译:一种使用局部代理模型来控制故障的优化液压成型结构的概率方法

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A probabilistic approach is proposed to optimise hydroformed structures by taking into account the potential variabilities. An efficient implementation requires an appropriate strategy for uncertainty representation and propagation. Moreover, the probability of failure associated to each failure mode must be accurately estimated. To this end, the failure modes are controlled locally only at the highly strained regions Which reduces the problem complexity and increases the precision of the generated surrogate models. In this study, finite element simulations with material formability diagrams are used to predict the critical zones in which failure modes may initiate. The predicted zones agree well with the experimental and numerical simulations. By this simplification, the optimisation problem is formulated differently while retaining the relevant physical features of the process. To illustrate this strategy, tee-shaped tube hydroforming process is proposed due to its complexity to demonstrate the benefits of the probabilistic approach. The optimisation problem is formulated within deterministic and probabilistic frameworks to determine the optimal loading paths. It will be shown that probabilistic optimum allows better process mechanics and improved thickness distribution in the hydroformed tube. This approach can be extended to other metal forming processes and easily implemented for industrial products within reasonable computational time. (C) 2015 Elsevier Ltd. All rights reserved.
机译:考虑到潜在的变化性,提出了一种概率方法来优化液压成型结构。有效的实施需要不确定性表示和传播的适当策略。此外,必须准确估计与每种故障模式相关的故障概率。为此,仅在高应变区域局部控制故障模式,这降低了问题的复杂性并提高了生成的替代模型的精度。在这项研究中,具有材料可成形性图的有限元模拟被用来预测可能引发破坏模式的关键区域。预测区域与实验和数值模拟非常吻合。通过这种简化,优化问题的表达方式有所不同,同时保留了过程的相关物理特征。为了说明此策略,由于其复杂性,提出了T形管液压成形工艺,以证明概率方法的好处。在确定性和概率性框架内制定了优化问题,以确定最佳加载路径。可以证明,概率最优允许在液压成型管中具有更好的工艺力学和改进的厚度分布。这种方法可以扩展到其他金属成型工艺,并且可以在合理的计算时间内轻松地用于工业产品。 (C)2015 Elsevier Ltd.保留所有权利。

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