Publication detail

High Quality Steel Casting by Using Advanced Mathematical Methods

MAUDER, T. ŠTĚTINA, J.

Original Title

High Quality Steel Casting by Using Advanced Mathematical Methods

English Title

High Quality Steel Casting by Using Advanced Mathematical Methods

Type

journal article in Web of Science

Language

en

Original Abstract

The main concept of this paper is to utilize advanced numerical modelling techniques with self-regulation algorithm in order to reach optimal casting conditions for real-time casting control. Fully 3-D macro-solidification model for the continuous casting (CC) process and an original fuzzy logic regulator are combined. The fuzzy logic (FL) regulator reacts on signals from two data inputs, the temperature field and the historical steel quality database. FL adjust the cooling intensity as a function of casting speed and pouring temperature. This approach was originally designed for the special high-quality high-additive steel grades such as higher strength grades, steel for acidic environments, steel for the offshore technology and so forth. However, mentioned approach can be also used for any arbitrary low-carbon steel grades. The usability and results of this approach are demonstrated for steel grade S355, were the real historical data from quality database contains approximately 2000 heats. The presented original solution together with the large steel quality databases can be used as an independent CC prediction control system.

English abstract

The main concept of this paper is to utilize advanced numerical modelling techniques with self-regulation algorithm in order to reach optimal casting conditions for real-time casting control. Fully 3-D macro-solidification model for the continuous casting (CC) process and an original fuzzy logic regulator are combined. The fuzzy logic (FL) regulator reacts on signals from two data inputs, the temperature field and the historical steel quality database. FL adjust the cooling intensity as a function of casting speed and pouring temperature. This approach was originally designed for the special high-quality high-additive steel grades such as higher strength grades, steel for acidic environments, steel for the offshore technology and so forth. However, mentioned approach can be also used for any arbitrary low-carbon steel grades. The usability and results of this approach are demonstrated for steel grade S355, were the real historical data from quality database contains approximately 2000 heats. The presented original solution together with the large steel quality databases can be used as an independent CC prediction control system.

Keywords

fuzzy logic, continuous casting, Optimal Cooling, steel quality prediction

Released

04.12.2018

Publisher

MDPI

Pages from

1

Pages to

13

Pages count

13

URL

Full text in the Digital Library

Documents

BibTex


@article{BUT151794,
  author="Tomáš {Mauder} and Josef {Štětina}",
  title="High Quality Steel Casting by Using Advanced Mathematical Methods",
  annote="The main concept of this paper is to utilize advanced numerical modelling techniques with self-regulation algorithm in order to reach optimal casting conditions for real-time casting control. Fully 3-D macro-solidification model for the continuous casting (CC) process and an original fuzzy logic regulator are combined. The fuzzy logic (FL) regulator reacts on signals from two data inputs, the temperature field and the historical steel quality database. FL adjust the cooling intensity as a function of casting speed and pouring temperature. This approach was originally designed for the special high-quality high-additive steel grades such as higher strength grades, steel for acidic environments, steel for the offshore technology and so forth. However, mentioned approach can be also used for any arbitrary low-carbon steel grades. The usability and results of this approach are demonstrated for steel grade S355, were the real historical data from quality database contains approximately 2000 heats. The presented original solution together with the large steel quality databases can be used as an independent CC prediction control system.",
  address="MDPI",
  chapter="151794",
  doi="10.3390/met8121019",
  howpublished="online",
  institution="MDPI",
  number="12",
  volume="8",
  year="2018",
  month="december",
  pages="1--13",
  publisher="MDPI",
  type="journal article in Web of Science"
}