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Ductility, Formability and Microstructure of Alloys and Steels

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Metals and Alloys".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 1443

Special Issue Editors


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Guest Editor
Pro Labor d.o.o., Podvin 20, 3310 Žalec, Slovenia
Interests: material thermodynamics; thermal analysis; hot rolling; HSLA; secondary metallurgy
Institute of Metals and Technology, Lepi pot 11, SI-1000 Ljubljana, Slovenia
Interests: steelmaking; metallurgy; processing; non-metallic inclusions; steel microstructure; alloys
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Special Issue Information

Dear Colleagues,

The continuous development of new products demands continuous improvements in steels and special alloys for target applications. Improving the mechanical properties and related functional properties are always a major focus in the field of metallurgy. Many factors affect the final properties and are considered in the entire production process from the chosen raw materials and alloy additions, scrap, the melting practice, melt-refining operations, casting control, plastic deformation for grain refining and texturing, and control over phase transformations during and after deformation (hot rolling, regular or precise hot forging, etc.) or implementing special thermo-mechanical treatments, high-deformation techniques, special off-line heat treatments, and others. An important functional property among many in the sheet, plate, wire, and beam production is formability, while ductility is the most important material property that can be measured on industrial or laboratory-prepared samples. The values are highly related to the surface (and sample) preparation, nominal and local compositions, surface quality, presence of internal defects, overall production history, strain state, and more. This Special Issue focuses on the Ductility, Formability and Microstructure of Alloys and Steels.

As ductility is an essential property for formability, it is measured using close to static (low strain rates) and dynamic methods (high strain rates) or using simplified observation experiments such as wedge tests and/or laboratory strip rolling at various working temperatures by observing the edge integrity or performing hot (Gleeble) compression tests, press bending, etc. Especially, free or roll bending is used as a typical manufacturing process for forming complex parts of more or less ductile metals and the forming behavior is often investigated (FLDs). However, not all thickness ranges of various sheets of steel or alloys are formed into a product by plastic deformation. Nevertheless, ductility still has an important role in the final integrity of products, due to the local plastic deformation/local formability present after intense arc welding, thermal cutting operations, heat treatments, and other operations.

In the case of powder metallurgy, the quality of the metal powder is important for certain characteristics such as roundness, circularity, oxygen covering, internal granulate defects, etc., affecting the final additively manufactured product under given or tested process parameters that are defined by the porosity percentage, microstructure formation, non-metallic inclusions formation concerning the ductility at room or elevated temperature, etc.

In the case of tool steels, the ability to undergo plastic deformation without cracking is important for a tool’s life expectancy. The steels are usually produced with a focus on cleanliness by using advanced melting processes. Therefore, ductility plays an important role as tools need the highest cleanliness and sufficient elemental (chemical) homogeneity for the heat treatment or chemo‒thermal response, microstructure evolution at different production steps, and for preventing premature failures such as for high-pressure die casting, extrusion, and press hardening or hot forming. Ductility also has high importance for shape castings as the material has no additional hot deformation treatments and ductility is related mainly to manufacturing melting practice, casting, chosen composition, etc.

Dr. Grega Klančnik
Dr. Jaka Burja
Guest Editors

Manuscript Submission Information

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Keywords

  • ductility of materials
  • formability
  • non-metallic inclusions
  • grain size evolution
  • microstructure evolution
  • steels
  • alloys
  • hot rolling
  • casting
  • work hardening

Published Papers (2 papers)

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12 pages, 20850 KiB  
Article
Microstructure and Wear Resistance of Si-TC4 Composite Coatings by High-Speed Wire-Powder Laser Cladding
by Boxuan Men, Shenzhen Sun, Chunyang Hu, Qi Zhang and Bin Han
Materials 2024, 17(5), 1126; https://doi.org/10.3390/ma17051126 - 29 Feb 2024
Viewed by 520
Abstract
The hardness and wear resistance of the surface of TC4 titanium alloy, which is widely used in aerospace and other fields, need to be improved urgently. Considering the economy, environmental friendliness, and high efficiency, Si-reinforced Ti-based composite coatings were deposited on the TC4 [...] Read more.
The hardness and wear resistance of the surface of TC4 titanium alloy, which is widely used in aerospace and other fields, need to be improved urgently. Considering the economy, environmental friendliness, and high efficiency, Si-reinforced Ti-based composite coatings were deposited on the TC4 surface by the high-speed wire-powder laser cladding method, which combines the paraxial feeding of TC4 wires with the coaxial feeding of Si powders. The microstructures and wear resistance of the coatings were analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), Vickers hardness tester, and friction and wear tester. The results indicate that the primary composition of the coating consisted of α-Ti and Ti5Si3. The microstructure of the coating underwent a notable transformation process from dendritic to petal, bar, and block shapes as the powder feeding speed increased. The hardness of the composite coatings increased with the increasing Si powder feeding rate, and the average hardness of the composite coating was 909HV0.2 when the feeding rate reached 13.53 g/min. The enhancement of the microhardness of the coatings can be attributed primarily to the reinforcing effect of the second phase generated by Ti5Si3 in various forms within the coatings. As the powder feeding speed increased, the wear resistance initially improved before deteriorating. The optimal wear resistance of the coating was achieved at a powder feeding rate of 6.88 g/min (wear loss of 2.55 mg and friction coefficient of 0.12). The main wear mechanism for coatings was abrasive wear. Full article
(This article belongs to the Special Issue Ductility, Formability and Microstructure of Alloys and Steels)
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17 pages, 1835 KiB  
Article
Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning
by Sandesh Risal, Navdeep Singh, Yan Yao, Li Sun, Samprash Risal and Weihang Zhu
Materials 2024, 17(3), 601; https://doi.org/10.3390/ma17030601 - 26 Jan 2024
Viewed by 545
Abstract
The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive [...] Read more.
The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy’s composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys. Full article
(This article belongs to the Special Issue Ductility, Formability and Microstructure of Alloys and Steels)
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