Special Issue "Researches and Simulations in Steel Rolling"

A special issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: 30 June 2018

Special Issue Editor

Guest Editor
Assoc. Prof. Dr. Adam Grajcar

Institute of Engineering Materials and Biomaterials, Faculty of Mechanical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Website | E-Mail
Interests: advanced high-strength steels; high-strength low-alloyed steels; thermomechanical processing; hot rolling; hot-working phenomena; physical simulation; deformation of metals

Special Issue Information

Dear Colleagues,

Steel is the world’s most popular metal alloy. We can easily predict that this material will remain the most common metal alloy of the large-scale production in the 21st century; this is because steels are used in every part of the industry, beginning from low-carbon sheet steels for automotive applications, through structural steels for bridges, buildings, linepipes, ships, pressure vessels, etc., engineering steels, stainless steels, specialty steels, to tool steels. At the same time, most of the products are used as plates, sheets, bars, rods, wires, sections, and rails. All of them require rolling to form a semi-final product from a slab, billet, ingot, etc. Simultaneously to the dimention changes, a microstructure of the products is formed during subsequent casting, hot rolling and very often cold rolling. The advanced steels are usually produced in modern integrated technological lines to satisfy both high quality requirements and cost effectiveness. Physical and numerical simulations are effective tools, which enable to easily go from an experimental part of the research to an industrial reality.

This Special Issue will cover recent progress and new developments in researches and simulations in steel rolling including its all metallurgical and technological aspects. Researches and simulations on microstructure-property relationships of hot-rolled, thermomechanically processed and cold-rolled steels, as well as selected technological aspects of modern rolling mills for flat and long products and researches on gauge, profile, flatness, and surface quality inspection are covered.

Assoc. Prof. Dr. Adam Grajcar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • steel rolling
  • hot-rolling of steel
  • cold-rolling of steel
  • thermomechanical processing of steel
  • physical simulation
  • numerical simulation
  • semi-industrial simulation
  • modern rolling mills

Published Papers (2 papers)

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Research

Open AccessArticle A Numerical Study on Contact Condition and Wear of Roller in Cold Rolling
Metals 2017, 7(9), 376; doi:10.3390/met7090376
Received: 7 July 2017 / Revised: 10 September 2017 / Accepted: 11 September 2017 / Published: 15 September 2017
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Abstract
An accurate determination of the contact pressure and local sliding in a cold rolling process is an essential step towards the prediction of the roller’s life due to wear damage. This investigation utilized finite element analysis to quantify the local contact pressure and
[...] Read more.
An accurate determination of the contact pressure and local sliding in a cold rolling process is an essential step towards the prediction of the roller’s life due to wear damage. This investigation utilized finite element analysis to quantify the local contact pressure and local sliding over the rolling bite in a plate cold rolling process. It was the first study to quantify the local sliding distance in a rolling process using the Finite Element Analysis (FEA). The numerical results indicate that the local contact pressure over the rolling bite demonstrates a hill profile, and the peak coincides with the neutral plane. The local sliding distance over the rolling bite demonstrates a double-peak profile with the two peaks appearing at the forward slip and backward slip zones respectively. The amplitude of sliding distance in the backward slip zone is larger than that in the forward slip zone. A stick zone was confirmed between the forward slip and backward slip zones. According to a parametric study, the local contact pressure and sliding distance decrease when the thickness reduction is reduced or the diameter of the roller is decreased. The location of the neutral plane always presents at the rolling exit side of the rolling bite’s center. The size of the stick zone enlarges and the sizes of slip zones shrink significantly when the friction coefficient is increased. Finally, a novel concept of wear intensity was defined to examine the wear of the roller based on the local contact pressure and local sliding distance. The results show that a two-peak wear response exists in the backward and forward slip zones. The magnitude of the wear in the backward slip zone is larger than that in the forward slip zone. For a given roller and blank material combination, using a smaller thickness reduction, a smaller diameter roller and a higher friction coefficient condition can reduce the wear of the roller for a single rolling cycle. The current paper develops an understanding of rolling contact responses to the wear of the roller in rolling process. The research method can also be applied to study other rolling or sliding wear problems. Full article
(This article belongs to the Special Issue Researches and Simulations in Steel Rolling)
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Open AccessArticle An Algorithm for Surface Defect Identification of Steel Plates Based on Genetic Algorithm and Extreme Learning Machine
Metals 2017, 7(8), 311; doi:10.3390/met7080311
Received: 30 June 2017 / Revised: 5 August 2017 / Accepted: 8 August 2017 / Published: 15 August 2017
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Abstract
Defects on the surface of steel plates are one of the most important factors affecting the quality of steel plates. It is of great importance to detect such defects through online surface inspection systems, whose ability of defect identification comes from self-learning through
[...] Read more.
Defects on the surface of steel plates are one of the most important factors affecting the quality of steel plates. It is of great importance to detect such defects through online surface inspection systems, whose ability of defect identification comes from self-learning through training samples. Extreme Learning Machine (ELM) is a fast machine learning algorithm with a high accuracy of identification. ELM is implemented by a hidden matrix generated with random initialization parameters, while different parameters usually result in different performances. To solve this problem, an improved ELM algorithm combined with a Genetic Algorithm was proposed and applied for the surface defect identification of hot rolled steel plates. The output matrix of the ELM’s hidden layers was treated as a chromosome, and some novel iteration rules were added. The algorithm was tested with 1675 samples of hot rolled steel plates, including pockmarks, chaps, scars, longitudinal cracks, longitudinal scratches, scales, transverse cracks, transverse scratches, and roll marks. The results showed that the highest identification accuracies for the training and the testing set obtained by the G-ELM (Genetic Extreme Learning Machine) algorithm were 98.46% and 94.30%, respectively, which were about 5% higher than those obtained by the ELM algorithm. Full article
(This article belongs to the Special Issue Researches and Simulations in Steel Rolling)
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