PREDICTION OF FRACTURE TOUGHNESS TEMPERATURE DEPENDENCE APPLYING NEURAL NETWORK

被引：0

作者：

Dlouhy, Ivo ^{[1
,2
]}

Hadraba, Hynek ^{[1
]}

Chlup, Zdenek ^{[2
]}

Smida, Tibor ^{[3
]}

机构：

[1] Acad Sci Czech Republ, Inst Phys Mat, Zizkova 22, Brno 61662, Czech Republic

[2] Brno Univ Technol, Inst Mat Sci & Engn, Fac Mech Engn, Brno 61669, Czech Republic

[3] IBOK As, Bratislava 83102, Slovakia

来源：

STRUCTURAL INTEGRITY AND LIFE-INTEGRITET I VEK KONSTRUKCIJA | 2011年 / 11卷 / 01期

关键词：

brittle to ductile transition; fracture toughness; artificial neural network; steels;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Reference temperature localizing the fracture toughness temperature diagram on temperature axis is predicted based on tensile test data. The regularisation neural network is developed to solve the correlation of these properties. Three-point bend specimens are applied to determine fracture toughness. The fracture toughness transition dependence is quantified by means of the master curve concept enabling to represent it by using one parameter, i.e. the reference temperature. Tensile samples with circumferential notch are also examined. In total 29 data sets from low-alloy steels are applied for the analysis. A good correlation of the predicted and experimentally determined values of the reference temperature is found.

引用

页码：9 / 14

页数：6

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