Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes
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. T...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Advances in Industrial and Manufacturing Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666912922000198 |
| _version_ | 1818214574722121728 |
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| author | Raphael Langbauer Georg Nunner Thomas Zmek Jürgen Klarner René Prieler Christoph Hochenauer |
| author_facet | Raphael Langbauer Georg Nunner Thomas Zmek Jürgen Klarner René Prieler Christoph Hochenauer |
| author_sort | Raphael Langbauer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T06:22:21Z |
| format | Article |
| id | doaj.art-b77f6fea07654a12b698946d274cdf0a |
| institution | Directory Open Access Journal |
| issn | 2666-9129 |
| language | English |
| last_indexed | 2024-12-12T06:22:21Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Industrial and Manufacturing Engineering |
| spelling | doaj.art-b77f6fea07654a12b698946d274cdf0a2022-12-22T00:34:53ZengElsevierAdvances in Industrial and Manufacturing Engineering2666-91292022-11-015100090Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipesRaphael Langbauer0Georg Nunner1Thomas Zmek2Jürgen Klarner3René Prieler4Christoph Hochenauer5Graz University of Technology, Institute of Thermal Engineering, Inffeldgasse 25/B, 8010, Graz, Austria; Corresponding author.Voestalpine Tubulars GmbH & Co KG, Alpinestraße 17, Kindberg, 8652, AustriaVoestalpine Tubulars GmbH & Co KG, Alpinestraße 17, Kindberg, 8652, AustriaVoestalpine Tubulars GmbH & Co KG, Alpinestraße 17, Kindberg, 8652, AustriaGraz University of Technology, Institute of Thermal Engineering, Inffeldgasse 25/B, 8010, Graz, AustriaGraz University of Technology, Institute of Thermal Engineering, Inffeldgasse 25/B, 8010, Graz, AustriaOne 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.http://www.sciencedirect.com/science/article/pii/S2666912922000198Temperature predictionArtificial neural networkSteel pipeHot rolling |
| spellingShingle | Raphael Langbauer Georg Nunner Thomas Zmek Jürgen Klarner René Prieler Christoph Hochenauer Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes Advances in Industrial and Manufacturing Engineering Temperature prediction Artificial neural network Steel pipe Hot rolling |
| title | Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes |
| title_full | Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes |
| title_fullStr | Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes |
| title_full_unstemmed | Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes |
| title_short | Development of an artificial neural network (ANN) model to predict the temperature of hot-rolled steel pipes |
| title_sort | development of an artificial neural network ann model to predict the temperature of hot rolled steel pipes |
| topic | Temperature prediction Artificial neural network Steel pipe Hot rolling |
| url | http://www.sciencedirect.com/science/article/pii/S2666912922000198 |
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