Cavitation Damage Prediction of Stainless Steels Using an Artificial Neural Network Approach

Cavitation damage has not been well predicted because of its complex relationship of many mechanical and microstructural factors. An artificial neural network approach of the back-propagation network was used to predict cavitation damage of stainless steels, 316L and 420, in terms of the significant influence of cavitation time, roughness, and residual stress on cavitation damage. Mean depth of erosion was used to quantitatively describe cavitation damage of 316L and 420. Prediction accuracy was improved by analyzing the effects of the number and type of input nodes, the number of nodes in the hidden layer, and different activation functions on prediction accuracy. The best performance was in the model with the input nodes of cavitation time and roughness, eleven nodes in the hidden layer, and the activation function of logsig.

由于许多机械和微观结构因素的复杂关系，气蚀损害尚未得到很好的预测。在空化时间，粗糙度和残留应力对气蚀损伤的显着影响方面，使用后传播网络的人工神经网络方法预测不锈钢，316L和420的气害损害。平均侵蚀深度用于定量描述316L和420的空化损害。通过分析输入节点的数量和类型的影响，隐藏层中的节点数量以及对预测准确性的不同激活功能，从而提高了预测精度。最好的性能是在模型中具有空化时间和粗糙度的输入节点，隐藏层中的11个节点以及Logsig的激活函数。