基于BP神经网络的V9-Cr4-Mo3高速钢冷轧辊磨损模型

利用自制轧辊模拟磨损试验机测试了6种不同碳含量的V9Cr4Mo3高速钢轧辊的磨损性能，利用BP神经网络建立了磨损量与碳含量和磨损时间的非线性关系模型．结果表明：良好训练的BP网络模型可以有效预测不同碳含量的V9Cr4Mo3高速钢轧辊的磨损性能．结果表明：碳含量约为2．58％时，高速钢基体组织主要为高硬度和高韧性的板条马氏体，可以有效抵御轧制过程中的疲劳和显微切削，耐磨性最佳；当碳含量过低时，高速钢基体为低硬度的铁素体，显微切削为轧辊的主要磨损机制，而碳含量过高时，其基体主要为韧性较差的片状马氏体，轧辊以疲劳磨损为主，二者均导致轧辊耐磨性下降．

Modeling of Wear Process of V9-Cr4-Mo3 High Speed Steel Cooling Roll Via BP Neural Network

Wear properties of six kinds of V9-Cr4-Mo3 high speed steel rolls with different carbon content were tested via cool-rolling wear simulationtesting machine developed by authors.By use of backpropagation (BP) neural network,the non-linear relationship model of the wear weight loss vs.carbon content and wear time was established based on experimental data. The results show that the well-trained BP neural network can predict wear properties of V9Cr4Mo3 high speed steel rolls effectively according to carbon content,and predict wear weight losses precisely according carbon contents and wear time. The prediction results indicate that the matrix microstructure of high speed steel is mainly lath martensite with good toughness and high hardness when carbon content is about 2.58%,resulting in the optimal wear property of roll.If carbon content is too low,the wear failure of roll is mainly caused by severe micro-cutting because the matrix microstructure of high speed steel is ferrite with very low hardness,leading to the decrease of wear resistance.While,the matrix microstructure is mainly composed of plate martensite with poor toughness when carbon content is too high,and therefore it is easy for roll to fatigue,also resulting in poor wear property.