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Advances in Metallurgical Process Engineering
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Advances in Metallurgical Process Engineering

A Special Issue of Materials (ISSN 1996-1944) belonging to the section "Metals and Alloys".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 1071

Editors

Prof. Dr. Ganfeng Tu

E-Mail Website
Guest Editor
School of Metallurgy, Northeastern University, Shenyang 110819, China
Interests: rare earth metallurgy; comprehensive utilization of metallurgical resources; pyrometallurgical recovery of precious metals; preparation of borides; advanced ceramic materials
Dr. Daxue Fu

E-Mail Website
Guest Editor
School of Metallurgy, Northeastern University, Shenyang 110819, China
Interests: rare earth metallurgy; vacuum metallurgy; molten salt electrolysis; comprehensive utilization of metallurgical resources; high-temperature mechanochemical synthesis

Special Issue Information

Dear Colleagues,

This Special Issue of the international academic journal of Materials will focus on the frontier development of metallurgical engineering and technology. It aims to become a core exchange platform for innovative research and engineering practice in the field, committed to promoting breakthroughs in basic theory and key technologies for metallurgical processes toward higher efficiency, greener operation, and intelligent development. The Special Issue covers high-level original research on the whole chain of metal extraction, preparation, processing and recycling; promotes interdisciplinary integration, especially the intersection of materials science, process simulation, artificial intelligence and low-carbon technologies; and builds bridges for global scholars and engineers to share innovative solutions to resource, energy and environmental challenges. The journal adheres to rigorous academic standards, encourages forward-looking and practical exploration and is committed to promoting knowledge sharing, accelerating the transformation of scientific discoveries into industrial innovation, and ultimately serving the high-quality sustainable development of the metallurgical industry.

Prof. Dr. Ganfeng Tu
Dr. Daxue Fu
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly 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

  • green metallurgy
  • novel metallurgical processes
  • comprehensive utilization of metallurgical resources
  • intelligent metallurgy
  • numerical simulation
  • integrated metallurgy and materials

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Published Papers (2 papers)

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Research

20 pages, 25058 KB  
Article
Electrochemical Endpoint Determination and Machine-Learning Prediction of Pickling Time for Hot-Rolled Automotive High-Strength Steel
by Zhou Xu, Jianfei Xu, Dongdong Ye, Changdong Yin, Yiwen Wu, Qiang Liu, Xinchun Huang, Longhai Liu and Jianjun Chen
Materials 2026, 19(18), 3974; https://doi.org/10.3390/ma19183974 (registering DOI) - 18 Sep 2026
Abstract
Accurate determination and prediction of pickling time are essential for preventing under-pickling and over-pickling and for improving the surface quality of hot-rolled high-strength steel. In this study, an electrochemical endpoint detection method combined with hybrid machine-learning models was developed to predict the pickling [...] Read more.
Accurate determination and prediction of pickling time are essential for preventing under-pickling and over-pickling and for improving the surface quality of hot-rolled high-strength steel. In this study, an electrochemical endpoint detection method combined with hybrid machine-learning models was developed to predict the pickling time of hot-rolled automotive high-strength steel. The variation in open-circuit potential during hydrochloric-acid pickling was monitored using an electrochemical workstation, and a potential-derivative near-zero method was proposed to determine the completion of oxide-scale removal. Based on repeated experiments under ten representative process conditions, a potential-derivative threshold of −5 × 10−4 V/s was adopted as the operational criterion for identifying the pickling endpoint. The effects of oxide-scale thickness, HCl concentration, pickling temperature, and accelerator concentration on pickling time were subsequently investigated. To describe the nonlinear relationship between these variables and pickling time, BP, GA-BP, ELM, and PSO-ELM regression models were established. The 48-observation dataset was evaluated using leakage-free grouped nested six-fold cross-validation repeated ten times, with all observations from the same strip group kept within the same fold. Among the investigated models, PSO-ELM exhibited the best prediction performance, achieving R2 = 0.85 ± 0.05, MAE = 7.27 ± 1.01 s, MAPE = 0.14 ± 0.02, and RMSE = 9.65 ± 1.48 s. These quantities are regression-performance statistics and are not interpreted as the percentage prediction accuracy. The proposed endpoint criterion and regression framework provide a laboratory-scale basis for data-driven pickling-time estimation within the investigated material and process ranges, and broader industrial application requires validation using larger multi-grade production datasets. Full article
(This article belongs to the Special Issue Advances in Metallurgical Process Engineering)
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24 pages, 5026 KB  
Article
Influence of Sintering and Heat Treatment on the Microstructure, Mechanical Properties, and Tribological Performance of AlTiN-Coated PM M42 High-Speed Steel
by Zijun Qi, Yi Chen, Ji Li, Yongde Huang, Qian Wang, Qi Wei, Xiaofeng Yang and Qiang Liu
Materials 2026, 19(8), 1667; https://doi.org/10.3390/ma19081667 - 21 Apr 2026
Viewed by 676
Abstract
Preparing a highly wear-resistant AlTiN coating on a powder metallurgy (PM) M42 high-speed steel substrate is a key strategy to enhance tool performance and meet the demands of efficient machining. This study adopted a process route comprising substrate preparation, heat treatment regulation, and [...] Read more.
Preparing a highly wear-resistant AlTiN coating on a powder metallurgy (PM) M42 high-speed steel substrate is a key strategy to enhance tool performance and meet the demands of efficient machining. This study adopted a process route comprising substrate preparation, heat treatment regulation, and arc-PVD deposition of AlTiN coatings to systematically investigate the influence of sintering temperature (1130, 1160, and 1190 °C) and austenitizing time (1150 °C for 0, 15, 60, and 120 min) on the microstructure and mechanical properties of the substrate, as well as on the tribological performance of the AlTiN coatings. The results indicate that elevating the sintering temperature promotes densification of the matrix, with Vickers hardness increasing from 366 HV to 462 HV and bending strength (σ) increasing from 1064 MPa to 1310 MPa. The predominant carbide phases identified are MC, M2C, and M6C. During austenitizing, microstructural changes consistent with a progressive transformation from M2C to MC and M6C carbides were indicated by SEM and XRD analyses. Precipitation strengthening was most evident after 60 min, with hardness reaching 868 HV. In contrast, bending strength (σ) exhibited a progressive decline with increasing austenitizing time, decreasing from 1310 MPa to 1015 MPa after 120 min, illustrating a clear trade-off between hardness and toughness. The wear behavior of the coating is governed synergistically by substrate hardness, bending strength (σ), coating–substrate interfacial adhesion strength (LC), and carbide phase transformation. Elevated substrate hardness enhances anti-wear performance; bending strength influences crack propagation and spallation tendency; and LC determines the efficiency of interfacial load transfer. The carbide phase evolution appears to modulate the coating’s wear behavior by regulating both the microstructure and mechanical properties of the substrate. Among the six sample conditions evaluated, the A3 sample (sintered at 1190 °C and austenitized for 120 min) exhibited the lowest wear rate (2.38 × 10−6 mm3·N−1·m−1), demonstrating superior wear resistance. These findings provide a reference for process optimization and rational design of M42/AlTiN composite coating systems. Full article
(This article belongs to the Special Issue Advances in Metallurgical Process Engineering)
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