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首页> 外文期刊>Advances in Mechanical Engineering >Optimization of Spot-Welded Joints Combined Artificial Bee Colony Algorithm with Sequential Kriging Optimization
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Optimization of Spot-Welded Joints Combined Artificial Bee Colony Algorithm with Sequential Kriging Optimization

机译：点焊联合人工蜂群算法与顺序克里格优化的优化

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摘要

Generally, spot-welded joints are the weakest parts of structures leading to fatigue failure under fluctuating loads. Therefore, it is important to optimize the spot weld to improve the fatigue life. However, a classical optimization of the spot weld often directly couples finite element analysis (FEA) with optimization algorithm, which may fall into a local optimum or be expensive computationally. In this study, a metamodel-based optimization procedure is proposed to find the optimum locations of spot-welded joints for maximum fatigue life. Based on the initial training points, Kriging model is implemented to approximate the objective function regarding the design variables (i. e., locations of spot welds). To further overcome the defect of traditional Kriging model and improve the accuracy of optimumresults, the sequential Kriging optimization (SKO) is utilized, where the Kriging model is updated iteratively by adding new training points to the training dataset till the global optimum is obtained. The optimization is run using artificial bee colony (ABC) algorithm and the results show that our proposed method is able to improve the performance of the spot-welded joint. More importantly, more competent optimum can be found and the optimization can be executed more efficiently, compared to the conventional methods.
机译：通常，点焊接头是结构最薄弱的部分，会导致载荷波动引起的疲劳破坏。因此，优化点焊以提高疲劳寿命很重要。然而，点焊的经典优化通常将有限元分析（FEA）与优化算法直接结合在一起，这可能会陷入局部最优或计算昂贵。在这项研究中，提出了一种基于元模型的优化程序，以找到点焊接头的最佳位置，以实现最大的疲劳寿命。基于初始训练点，实施克里格模型以近似关于设计变量（即，点焊的位置）的目标函数。为了进一步克服传统Kriging模型的缺陷并提高最优结果的准确性，采用了顺序Kriging优化（SKO），通过向训练数据集添加新的训练点来迭代Kriging模型，直到获得全局最优为止。使用人工蜂群算法进行了优化，结果表明我们提出的方法能够提高点焊接头的性能。更重要的是，与传统方法相比，可以找到更合适的最优值，并且可以更有效地执行优化。

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