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板条马氏体钢的断裂韧性与缺口韧性、拉伸塑性的关系

梁益龙 , 雷旻 , 钟蜀辉 , 江山

金属学报

研究了超高强度板条马氏体钢的平面应变断裂韧性与缺口韧性、拉伸塑性之间的关系。实验结果表明，断裂韧性受控于裂尖前沿很小范围内（1—2倍临界裂纹张开位移c）显微组织的微观塑性，钝缺口韧性、拉伸塑性则受控于奥氏体晶粒尺寸或板条束直径。特征距离与裂纹尖端钝化所产生的有效变形区尺寸（2c）相对应。并提出断裂韧性与缺口韧性、拉伸塑性的关系取决于尖裂纹前沿有效变形区尺寸（2c）与原奥氏体晶粒尺寸（dγ）的相对大小。当2c＞（1－2dγ）时，两者的变化一致

关键词: 板条马氏体钢 , austenite grain size , fracture toughness , notch toughness , tensile ductility

A NEURAL NETWORK-BASED MODEL FOR PREDICTION OF HOT-ROLLED AUSTENITE GRAIN SIZE AND FLOW STRESS IN MICROALLOY STEEL

J. T.Niu , L.J.Sun and P.Karjalainen 1) Harbin Institute of Technology , Harbin 150001 , China 2) University of Oulu , FIN-90571 , Oulu , Finland

金属学报(英文版)

For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of BP algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.

关键词: microalloy steel , null , null , null , null , null

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