In this study, mechanical properties of hypereutectoid steels after thermomechanical processing have been evaluated using standard mechanical tests and a theoretical model has been developed using a novel artificial neural network approach. The K-folder cross validation (K-CV) was used to improve the generalized regression neural network (GRNN) to model and predict the lamellar spacing and mechanical properties of hypereutectoid steels with a small sample data. The independent variables in the model were the alloying elements. The dependent parameters were the interlamellar spacing, tensile strength, yield strength, section shrinkage and hardness. A comparison between the predicted values by improved GRNN with the experimental data indicates that the well trained model can provide accurate results. The effects of alloying elements can be evaluated by the developed model and help to achieve the desired lamellar spacing and mechanical properties. This would help the materials engineer suitably design the alloying compositions to obtain the desired combination of plasticity and strength properties.

在这项研究中，使用标准的机械测试评估了超共析钢在热机械加工后的机械性能，并使用新型的人工神经网络方法建立了理论模型。K文件夹交叉验证（K-CV）用于改进广义回归神经网络（GRNN），以少量样本数据对超共析钢的层状间距和力学性能进行建模和预测。模型中的自变量是合金元素。相关参数是层间间距，拉伸强度，屈服强度，截面收缩率和硬度。通过改进的GRNN将预测值与实验数据进行比较表明，训练有素的模型可以提供准确的结果。合金元素的影响可以通过开发的模型进行评估，并有助于实现所需的层状间距和机械性能。这将有助于材料工程师适当地设计合金成分，以获得所需的塑性和强度性能组合。