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Metals
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Computational Methods in Metallic Materials Manufacturing Processes 2025
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Computational Methods in Metallic Materials Manufacturing Processes 2025

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1358

Special Issue Editor

Dr. Uroš Župerl

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: control systems; cyber–physical systems; machining; optimization; modeling; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing complexity of manufacturing processes of metallic materials causes significant difficulties in their optimization, modeling and control. The most innovative way to modernize these manufacturing processes is to introduce advanced computational methods. Emerging technologies such as machine learning, artificial intelligence, cloud computing, the Internet of Things and cognitive systems have the potential to transform manufacturing processes of metallic materials to a more efficient level.

This Special Issue of Metals will cover recent advances in the modeling, optimization and control of different subprocesses in metallic material manufacturing, including casting, rolling, heat treating, machining, product delivery and quality assurance, while considering the most recent experimentally obtained processing data. Practical applications are especially welcome, and research with results from the industrial environment is desirable.

Prof. Dr. Uroš Župerl
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

  • metallic materials
  • manufacturing
  • metallurgy
  • machining
  • modeling
  • optimization and control
  • computational methods
  • cost reduction
  • quality of products
  • industrial case studies

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Published Papers (1 paper)

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Research

14 pages, 2603 KiB  
Article
Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking
by Quantum Zhuo, Mansour N. Al-Harbi and Petrus C. Pistorius
Metals 2025, 15(1), 13; https://doi.org/10.3390/met15010013 - 28 Dec 2024
Viewed by 1085
Abstract
The importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year’s operational data from an EAF plant that uses a large proportion of direct-reduced iron [...] Read more.
The importance of electric arc furnace (EAF) steelmaking is expected to increase worldwide as parts of the industry transition to lower carbon dioxide emissions. This work analyzed one year’s operational data from an EAF plant that uses a large proportion of direct-reduced iron (DRI) in the furnace feed. The data were used to test different approaches to quantifying the effects of process conditions on specific electricity consumption (kWh per ton of crude steel). In previous work, inputs such as the proportion of DRI, fluxes, natural gas, and oxygen were linearly correlated with the specific electricity consumption. The current work has confirmed that conventional multiple linear regression (MLR) reproduces electricity consumption trends in EAF steelmaking, but many model coefficients deviated significantly from expected values and appeared unphysical. The implementation of engineered features—the slag volume and total carbon input—in an MLR model resulted in coefficients that were closer to expectations, but did not improve prediction accuracy. Further improvement was obtained by applying the engineered features to a non-linear machine-learned model (based on XGBoost), yielding both physically reasonable trends and smaller prediction errors. Trends from Shapley dependence analysis (applied to the XGBoost model) are quantitatively consistent with theoretical trends. These include the energy needed to melt slag, and the endothermic effect of carbon additions. The fitted models demonstrate the potential to diagnose poor slag foaming by showing an increase in electricity consumption with increased oxygen use. This example demonstrates that practically important steelmaking process insights inferred via a linear regression approach can be improved by applying Shapley analysis to a machine-learned model based on engineered features. Full article
(This article belongs to the Special Issue Computational Methods in Metallic Materials Manufacturing Processes 2025)
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