L. P. Karjalainen (Department of Mechanical Engineering
,
University of Oulu
,
Oulu
,
Finland)
金属学报(英文版)
Modelling has become a more and more valuable tool in the design, control and development of steel processing. Empirical regression equations, physically based approachs, artificial neural networks and hybrid models are being theied in computer modelling. In all cases, relevant data are necessary, which can be most economically obtained by physical simulation. Physical simulation with a Gleeble simulator has been used in a large number of tasks at the University of Oulu for ten years in cooperotion with the Finnish metals industry. Some examples of these will be described and discussed below, such as the optimization of the recrystallization controlled rolling process, the improvement of the hot strength model for the control of coiling tension and the optimization of continuous strip annealing schedules.Finally,brief remarks will be then on a couple of projects now under way.
关键词:
physical simulation
,
null
,
null
,
null
,
null
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
机械工程材料
"第七届材料与热加工物理模拟及数值模拟国际学术会议"定于2013年6月16~19日在芬兰奥卢大学(Oulu University)举行,奥卢大学是北欧最著名的综合性大学之一,奥鲁市位于北欧的北部,毗邻北极圈和圣诞老人的故乡罗瓦涅米。
关键词:
国际学术会议
,
数值模拟
,
物理模拟
,
热加工
,
材料
,
综合性大学
,
北极圈
,
北欧