基于自适应模糊神经网络焊接接头力学性能预测

通过对TC4钛合金进行TIG焊,并测定接头的抗拉强度、抗弯强度和断后伸长率,获得网络仿真所需的数据.结合使用BP算法与最小二乘相结合的混合算法,建立了用于焊接接头力学性能预测的自适应模糊神经网络模型.利用该模型进行仿真,其平均误差远小于7%.结果表明,该模型可根据焊接工艺参数对焊接接头的抗拉强度、抗弯强度和断后伸长率等力学性能进行较为准确的预测,并且具有建模快、模型简单、预测速度快、预测精度高,泛化能力强的优点,从而为焊接接头力学性能预测提供了一条有效的途径.

Prediction mechanical properties of welded joints based on ANFIS

An method to predict the mechanical properties of welded joints based on adaptive fuzzy neural networks (ANFIS) was studied.By TC4 titanium welded by tungsten inert-gas welding, the tensile strength, bend strength and elongation of welded joints were tested as the data for networks simulation.With the hybrid algorithm of back propagation algorithm and least square algorithm, the model of adaptive fuzzy neural networks to predict mechanical properties of welded joints was established.The results of network simulation show that, the average error is far from less than 7%;according to welding parameter, the mechanical properties including tensile strength, bend strength and elongation can be predicted more accurately by this model;it has the merits of building model easily, simple structure, high precision and good generalization.Consequently, this method can provide an effective approach to estimate mechanical properties of welded joints.