Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes

Advances in Industrial and Manufacturing Engineering

Jürgen Klarner¹  Christoph Hochenauer¹  Georg Nunner²  Raphael Langbauer³  Thomas Zmek⁴  René Prieler⁴ 

[1] Co KG, Alpinestraße 17, Kindberg, 8652, Austria;Corresponding author.;Graz University of Technology, Institute of Thermal Engineering, Inffeldgasse 25/B, 8010, Graz, Austria;;Voestalpine Tubulars GmbH &

关键词: Temperature prediction;    Artificial neural network;    Steel pipe;    Hot rolling;   

DOI  :  

来源: DOAJ

【 摘 要 】

One important objective in steel pipe manufacturing is to avoid rejects. In order to adequately heat each individual pipe in the furnace, the surface temperature of all pipes after rolling must be predicted accurately. A fast model is needed that can provide this prediction quickly and repeatedly. To achieve this goal, artificial neural networks (ANN) were applied to the hot-rolling process used to create seamless steel pipes for the first time, and results are presented in this paper. Modelling the process is a complicated task, because a wide range of different geometries are manufactured, and the pipes can possibly be cooled after rolling. To address this issue, two ANN models were designed, with one model consisting of two coupled ANNs to increase its accuracy. This also represents a novel modelling approach. Both models were trained with data recorded during the production process. In general, the modelling results agree well with data collected by the in-plant measurement system for a wide range of different finished pipe geometries. The two models are compared, and differences in their behavior are discussed.

【 授权许可】

Unknown