Optimization of Spot-Welded Joints Combined Artificial Bee Colony Algorithm with Sequential Kriging Optimization

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.