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基于神经网络方法的焊接接头力学性能预测

Predication of properties of welded joints based on neural network

  • 摘要: 针对焊接过程严重非线性和焊材中多种成分的复杂交互作用使得对接头力学性能的准确估算十分困难的实际问题,论述了神经网络技术在焊接接头力学性能预测方面的应用。研究了神经网络建模方法,提出采用均匀设计法优化设计神经网络参数,在四类17种钢材的焊接热模拟数据基础上,建立了预测焊接接头力学性能的神经网络模型。试验表明该模型可根据钢材成分和焊接规范对焊接接头及其热影响区的冲击韧度、抗拉强度、屈服强度、断面收缩率和硬度等力学性能进行较为准确的估算。试验表明,该预测方法较之传统统计方法,预测精度有了大幅度提高,为实现焊接接头力学性能预测提供了一条有效的途径。

     

    Abstract: It is difficult to predict the mechanical properties of welded joints because of nonlinear in welding process and complicated mutual effects in multi-composition welding material. Based on these practical problems, the application of neural network technology in predicting mechanical properties of welding joints is developed.The modeling method has been studied and the author puts forward that the parameters of neutral network can be optimized by the method of uniform design. The neutral network model of mechanical properties of welded joints is established on the basis of the data of welding thermal simulation, the experimental results show that this model can predict the mechanical pronerties include impact toughness, tensile strength, subdued strength,reduction ratio of area and hardness more accurately. At the same time, using this method can improve estimating precision largely compared with using traditional statistic method. That is, this method can provide an effective approach to estimate the mechanical properties of welded joints.

     

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