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Fatigue behaviors prediction method of welded joints based on soft computing methods

机译：基于软计算方法的焊接接头疲劳行为预测方法

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摘要

In allusion to the highly nonlinear, multivariable, strong coupling and interaction of various factors of the welded process, it is difficult to predict the fatigue behaviors of welded joints. We make use of the capabilities and advantages of rough set theory, ant colony algorithm and BP neural network, a novel fatigue behaviors prediction method (RST-ACO-BPNN) of welded joints based on RST, ACO and BPNN is proposed in this paper. The proposed RST-ACO-BPNN method utilizes the knowledge reduction ability of rough set theory for dealing with the original fatigue sample data, the minimum fatigue feature subset is obtained. The ant colony algorithm with the ability of strong global search was used to optimize the weights of BP neural network for obtaining the optimized BP neural network model. Then the minimum reduced subset was inputted into the optimized BP neural network model to construct the novel fatigue behaviors prediction model of welded joints by the continuous training and adjusting. To verify the correctness and validity of the novel RST-ACO-BPNN prediction model by applying in aluminum alloy welded joints. The simulation results show the proposed method can predict effectively the fatigue behaviors of aluminum alloy welded joints. And the RST-ACO-BPNN prediction method is provided with the merits of building model easily, simple structure, high precision and good generalization. Consequently, the prediction method can provide an effective approach to predict the fatigue behaviors of welded joints.
机译：考虑到焊接过程中各种因素的高度非线性，多变量，强耦合和相互作用，很难预测焊接接头的疲劳行为。利用粗糙集理论，蚁群算法和BP神经网络的能力和优势，提出了一种基于RST，ACO和BPNN的焊接接头疲劳行为预测新方法（RST-ACO-BPNN）。所提出的RST-ACO-BPNN方法利用粗糙集理论的知识约简能力来处理原始疲劳样本数据，获得了最小疲劳特征子集。采用具有较强全局搜索能力的蚁群算法对BP神经网络的权重进行优化，得到优化的BP神经网络模型。然后将最小还原子集输入到优化的BP神经网络模型中，通过连续训练和调整，构造出新型的焊接接头疲劳行为预测模型。通过在铝合金焊接接头中应用，验证了新型RST-ACO-BPNN预测模型的正确性和有效性。仿真结果表明，该方法可以有效预测铝合金焊接接头的疲劳性能。 RST-ACO-BPNN预测方法具有建立模型容易，结构简单，精度高，推广性好的优点。因此，该预测方法可以提供一种有效的方法来预测焊接接头的疲劳行为。

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