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An adaptive algorithm for pass adaptation in plate rolling

Journal of University of Science and Technology Beijing 2002年第5期9卷 396-399页

作者： Zhichun Mu, WeimingLi, and Ke LiuInformation Engineering School, University of Science and Technology Beijing, Beijing 100083, China Information Engineering School University of Science and Technology Beijing Beijing 100083 China

A new algorithm for pass adaptation in plate rolling is developedto improve thickness accuracy of plate products. The feature of thealgorithm is that it uses the measured data rather than the schedulecalculated data in adaptation, which leads to notable improvem- entin prediction accuracy of the rolling parameters and thicknessaccuracy of products can be improved according. Results show thatthis adaptive algorithm is effective in practice.

关键词： adaptive algorithm plate rolling measured data

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Neural Network Based Adaptation Algorithm for Online Prediction of Mechanical Properties of Steel

引用

Journal of Computer Science Research 2020年第2期2卷 41-47页

作者： S.Rath Research&Development Centre for Iron&Steel(RDCIS) RanchiIndia

product is tested in a laboratory for its mechanical properties like yield strength(YS),ultimate tensile strength(UTS)and percentage elongation.This paper describes a mathematical model based method which can predict the mechanical properties without testing.A neural network based adaptation algorithm was developed to reduce the prediction error.The uniqueness of this adaptation algorithm is that the model trains itself very fast when predicted and measured data are incorporated to the model.Based on the algorithm,an *** based intranet website has also been developed for calculation of the mechanical properties.In the starting Furnace Module webpage,austenite grain size is calculated using semi-empirical equations of austenite grain size during heating of slab in a reheating furnace.In the Mill Module webpage,different conditions of static,dynamic and metadynamic recrystallization are calculated.In this module,austenite grain size is calculated from the recrystallization conditions using corresponding recrystallization and grain growth equations.The last module is a cooling module.In this module,the phase transformation equations are used to predict the grain size of ferrite phase.In this module,structure-property correlation is used to predict the final mechanical properties.In the Training Module,the neural network based adapation algorithm trains the model and stores the weights and bias in a database for future predictions.Finally,the model was trained and validated with measured property data.

关键词： Artificial Neural Network Mathematical model plate rolling Hybrid model

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