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Metals
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Applied Artificial Intelligence in Steelmaking
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Applied Artificial Intelligence in Steelmaking

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Extractive Metallurgy".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 15222

Special Issue Editor

Dr. Miha Kovačič

E-Mail Website
Guest Editor
1. Štore Steel d.o.o., Štore, Slovenia
2. Faculty of Mechanical Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: optimization; modeling; applied artificial intelligence; evolutionary computation; genetic algorithm; genetic programming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Steelmaking as the process of producing steel from iron ore or scrap is one of the major existing technologies, crucial also for the development of a future technological society. At the same time, the fourth industrial revolution has introduced artificial intelligence into all aspects of our everyday life. This Special Issue of Metals will cover the usage of artificial intelligence methods in all stages of the process, from steelmaking through casting to rolling, heat treating ,and delivery of the product (e.g., manipulation, transportation, logistics), including monitoring, quality assurance, and environmental issues. Practical applications are especially welcome.

Assoc. Prof. Dr. Miha Kovačič
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 submissions that pass pre-check are 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 2600 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

  • Artificial Intelligence
  • steelmaking
  • applications
  • optimization
  • modeling

Published Papers (5 papers)

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Research

12 pages, 982 KiB  
Article
Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study
by Miran Brezocnik and Uroš Župerl
Metals 2021, 11(6), 972; https://doi.org/10.3390/met11060972 - 17 Jun 2021
Cited by 4 | Viewed by 2264
Abstract
Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. [...] Read more.
Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (t-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model’s analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Steelmaking)
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13 pages, 1992 KiB  
Article
Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study
by Miha Kovačič, Shpetim Salihu, Gašper Gantar and Uroš Župerl
Metals 2021, 11(3), 426; https://doi.org/10.3390/met11030426 - 5 Mar 2021
Cited by 3 | Viewed by 2019
Abstract
In this paper, machinability influences from the start to end of final product production in a steel plant were analyzed, including chemical composition, deoxidizing agents and casting parameters, which drastically influence the macrostructure and segregation (i.e., chemical nonhomogeneity) of continuously cast and subsequently [...] Read more.
In this paper, machinability influences from the start to end of final product production in a steel plant were analyzed, including chemical composition, deoxidizing agents and casting parameters, which drastically influence the macrostructure and segregation (i.e., chemical nonhomogeneity) of continuously cast and subsequently rolled material. The data (seven parameters from secondary metallurgy, four parameters from the casting process and the content of ten chemical elements) from the serial production of calcium-treated steel grades (254 batches of 25 different steel grades from January 2018 to March 2020) were used for predicting machinability. Machinability was determined based on ISO 3685:1993, where the machinability of each individual batch is represented as the cutting speed and the tool is worn out within fifteen minutes. For the prediction of these cutting speeds, linear regression and genetic programming were used. Out of 25 analyzed steel grades, 20MnV6 steel grade was the most problematic and also the most often produced. Out of 57 produced batches of 20MnVS6 steel, 23 batches had nonconforming machinability. Based on the modeling results, the steelmaking process was optimized. Consequently, 40 additional batches of 20MnV6 (from March 2020 to July 2020) were subsequently produced based on an optimized steelmaking process. In all 40 cases, the required machinability was achieved without changing other properties required by the customers. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Steelmaking)
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19 pages, 4330 KiB  
Article
Comparison of Optimization-Regulation Algorithms for Secondary Cooling in Continuous Steel Casting
by Michal Brezina, Tomas Mauder, Lubomir Klimes and Josef Stetina
Metals 2021, 11(2), 237; https://doi.org/10.3390/met11020237 - 1 Feb 2021
Cited by 9 | Viewed by 3043
Abstract
The paper presents the comparison of optimization-regulation algorithms applied to the secondary cooling zone in continuous steel casting where the semi-product withdraws most of its thermal energy. In steel production, requirements towards obtaining defect-free semi-products are increasing day-by-day and the products, which would [...] Read more.
The paper presents the comparison of optimization-regulation algorithms applied to the secondary cooling zone in continuous steel casting where the semi-product withdraws most of its thermal energy. In steel production, requirements towards obtaining defect-free semi-products are increasing day-by-day and the products, which would satisfy requirements of the consumers a few decades ago, are now far below the minimum required quality. To fulfill the quality demands towards minimum occurrence of defects in secondary cooling as possible, some regulation in the casting process is needed. The main concept of this paper is to analyze and compare the most known metaheuristic optimization approaches applied to the continuous steel casting process. Heat transfer and solidification phenomena are solved by using a fast 2.5D slice numerical model. The objective function is set to minimize the surface temperature differences in secondary cooling zones between calculated and targeted surface temperatures by suitable water flow rates through cooling nozzles. Obtained optimization results are discussed and the most suitable algorithm for this type of optimization problem is identified. Temperature deviations and cooling water flow rates in the secondary cooling zone, together with convergence rate and operation times needed to reach the stop criterium for each optimization approach, are analyzed and compared to target casting conditions based on a required temperature distribution of the strand. The paper also contains a brief description of applied heuristic algorithms. Some of the algorithms exhibited faster convergence rate than others, but the optimal solution was reached in every optimization run by only one algorithm. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Steelmaking)
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13 pages, 7641 KiB  
Article
Determination of the Grain Size in Single-Phase Materials by Edge Detection and Concatenation
by Lucijano Berus, Plavka Skakun, Dragan Rajnovic, Petar Janjatovic, Leposava Sidjanin and Mirko Ficko
Metals 2020, 10(10), 1381; https://doi.org/10.3390/met10101381 - 16 Oct 2020
Cited by 10 | Viewed by 3942
Abstract
This paper presents a novel approach for edge detection and concatenation. It applies the proposed method on a set of optical microscopy images of aluminium alloy Al 99.5% (ENAW1050A) samples with different grain size values. The performance of the proposed approach is evaluated [...] Read more.
This paper presents a novel approach for edge detection and concatenation. It applies the proposed method on a set of optical microscopy images of aluminium alloy Al 99.5% (ENAW1050A) samples with different grain size values. The performance of the proposed approach is evaluated based on the intercept method and compared with the manual grain size determination method. Edge detection filters have proven inefficient in grain boundaries’ detection of the presented microscopy images. To some extent only the Canny edge-detection filter was able to compute grain boundaries of lower-resolution images adequately, while the presented method proved to be superior, especially in high-resolution images. The proposed method has proven its applicability, and it implies higher automatisation and lower processing times compared to manual optical microscopy image processing. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Steelmaking)
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23 pages, 19683 KiB  
Article
A Modular Machine-Learning-Based Approach to Improve Tensile Properties Uniformity Along Hot Dip Galvanized Steel Strips for Automotive Applications
by Valentina Colla, Silvia Cateni, Alessandro Maddaloni and Antonella Vignali
Metals 2020, 10(7), 923; https://doi.org/10.3390/met10070923 - 9 Jul 2020
Cited by 8 | Viewed by 3054
Abstract
The paper presents a machine learning-based system aimed at improving the homogeneity of tensile properties of steel strips for automotive applications over their strip length in the annealing and hot dip galvanizing lines. A novel modular approach is proposed exploiting process and product [...] Read more.
The paper presents a machine learning-based system aimed at improving the homogeneity of tensile properties of steel strips for automotive applications over their strip length in the annealing and hot dip galvanizing lines. A novel modular approach is proposed exploiting process and product data and combining smart data pre-processing and cleansing algorithms, an ensemble of neural networks targeted to specific product classes and an ad-hoc developed iterative procedure for identifying the variability ranges of the most relevant process variables. A decision support concept is implemented through a software tool, which facilitates exploitation by plant managers and operators. The system has been tested on site. The results show its effectiveness in improving the control of the thermal evolution of the strip with respect to the standard operating practice. Full article
(This article belongs to the Special Issue Applied Artificial Intelligence in Steelmaking)
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