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Metals
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Computer Methods in Metallic Materials (2nd Edition)
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Computer Methods in Metallic Materials (2nd Edition)

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

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 7766

Special Issue Editors

Prof. Dr. João Manuel R. S. Tavares

E-Mail Website
Guest Editor
Faculdade de Engenharia, Universidade do Porto, 4099-002 Porto, Portugal
Interests: artificial intelligence; data mining; machine learning; pattern recognition; simulation; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals
Dr. José Machado

E-Mail Website
Guest Editor
Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
Interests: structural adhesives; high strain rates; aging; fatigue; mechanical project; numerical simulation; structural analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The permanent development of computer methods is of great interest in the field of metallic materials, as its integration supports the increasing necessity to solve complex problems in numerical modelling involving physical phenomena. Numerous innovative techniques dedicated to the description or prediction of metallic materials’ behavior have been developed. The application of multiscale analysis (macro, micro, and nano) in the numerical simulation of intricate material dynamics phenomena has become available and effective. Moreover, advances in new methods and techniques regarding numerical simulation are of great importance to understand and adapt the development of metallic materials. Numerical tools can also be integrated in the field of material database and design, as well as fractographic classification through computer vision or manufacturing processes.

The aim of this Special Issue, “Computer Methods in Metallic Materials (Volume II)”, is to disseminate numerical advances which have been achieved through the development and integration of new software, numerical models, and simulation techniques. Other areas of interest are related to data processing and machine learning models, or non-destructive testing (NDT) techniques. The development of such computer methods enables the exploration and introduction of new areas of study within metallic materials, such as metal forming, casting, nanotechnology, additive manufacturing processes of metals, as well as optoelectronic, magnetic, electronic and imaging technologies. 

We are pleased to invite researchers, manufacturers, and end users to contribute to this Special Issue, which also welcomes review and perspective manuscripts.

Possible topics include, but are not limited to:

  • Artificial intelligence, big data, machine learning, and optimization techniques;
  • Computational methods, numerical model development, and simulation;
  • Computational techniques for modelling in control;
  • Computer methods for metallic materials development;
  • Computer methods for microstructural characterization;
  • Computer methods for non-destructive testing:
  • Image processing and analysis;
  • Numerical modelling and image analysis of microstructural evolution;
  • Pattern recognition and classification;
  • Software development for metallic materials.

Prof. Dr. João Manuel R. S. Tavares
Dr. José Machado
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 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

  • computer methods
  • computer model prediction
  • numerical simulation
  • software development
  • computer-aided materials design
  • materials characterization
  • NDT techniques
  • machine learning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

18 pages, 6367 KiB  
Article
Molecular Dynamics Study of Temperature Dependence of Grain Boundaries (100) in Pure Aluminum with Application of Machine Learning
by Evgenii V. Fomin
Metals 2024, 14(4), 415; https://doi.org/10.3390/met14040415 - 31 Mar 2024
Cited by 1 | Viewed by 1123
Abstract
As is known, grain boundary (GB) energy determines the mobility of GBs and their population in metals. In this work, we study the energy of GBs in the (100) crystallographic plane and in the temperature range from 100 to 700 K. The study [...] Read more.
As is known, grain boundary (GB) energy determines the mobility of GBs and their population in metals. In this work, we study the energy of GBs in the (100) crystallographic plane and in the temperature range from 100 to 700 K. The study is carried out using both the molecular dynamic (MD) method and machine learning approach to approximate the MD data in order to obtain functional dependence in the form of a feed-forward neural network (FCNN). We consider the tilt and twist grain boundaries in the range of misorientation angles from 0 to 90°. Also, we calculate the average and minimum energy over the ensemble of GB states, since there are many stable and metastable structures with different energies even at a fixed grain misorientation. The minimum energies decrease with increasing temperature, which is consistent with the results of other studies. The scatter of GB energies in the temperature range from 100 to 700 K is obtained on the basis of MD simulation data. The obtained energy spread is in reasonable agreement with the data from other works on the values of GB energy in pure aluminum. The predictive ability of the trained FCNN as well as its ability to interpolate between the energy and temperature points from MD data are both demonstrated. Full article
(This article belongs to the Special Issue Computer Methods in Metallic Materials (2nd Edition))
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14 pages, 4808 KiB  
Article
A Quantitative and Optimization Model for Microstructure Uniformity of Sinter Based on Multiple Regression-NSGA2
by Shilong Fang, Mingduo Li, Lei Liu, Xiuli Han, Bowen Duan and Liwen Qin
Metals 2024, 14(2), 169; https://doi.org/10.3390/met14020169 - 29 Jan 2024
Viewed by 1110
Abstract
The degree of homogeneity of the sintered ore phase structure directly determines its quality index. A sinter ore quality evaluation method based on the quantification of the homogeneity of the mineral phase structure is proposed. First, the magnetite particle size characteristics in the [...] Read more.
The degree of homogeneity of the sintered ore phase structure directly determines its quality index. A sinter ore quality evaluation method based on the quantification of the homogeneity of the mineral phase structure is proposed. First, the magnetite particle size characteristics in the ore phase structures with different degrees of homogeneity were summarized under a polarized light microscope, and a criterion for evaluating the uniformity of the sintered ore phase structure based on the magnetite content of different particle size grades was determined. Second, a multiple regression model was established for the raw material composition ratio of magnetite with varying particle size grades. Finally, the multiple regression model was optimized using the second-generation non-dominated sorting genetic algorithm (NSGA2). The results show that mineral phase structure analysis categorized the magnetite particle sizes into <30 μm, 30~60 μm, and >60 μm. The adjusted R2 of the multiple regression model of the chemical composition of raw materials and the proportion of magnetite of each particle size grade were all greater than 0.95, and the p values were all <0.05, indicating a high degree of model fitting. Using model analysis, the single factor and the interaction between the multiple factors that significantly influence the proportion of magnetite in the three particle size grades were determined. The multivariate regression model was optimized using the NSGA2 algorithm to determine the ratios of Al2O3 mass% = 1.82, MgO mass% = 1.50, and R(CaO mass%/SiO2 mass%) = 1.84 for the highest degree of uniformity of the sintered ores. Under this sintering condition, the micro-mineral phase structure became more homogeneous, confirming the model’s reliability. Full article
(This article belongs to the Special Issue Computer Methods in Metallic Materials (2nd Edition))
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14 pages, 6097 KiB  
Article
Application of Digital Image Processing Techniques to Detect Through-Thickness Crack in Hole Expansion Test
by Daniel J. Cruz, Rui L. Amaral, Abel D. Santos and João Manuel R. S. Tavares
Metals 2023, 13(7), 1197; https://doi.org/10.3390/met13071197 - 28 Jun 2023
Cited by 5 | Viewed by 2104
Abstract
Advanced high-strength steels (AHSS) have become increasingly popular in the automotive industry due to their high yield and ultimate tensile strengths, enabling the production of lighter car body structures while meeting safety standards. However, they have some setbacks compared to conventional steels, such [...] Read more.
Advanced high-strength steels (AHSS) have become increasingly popular in the automotive industry due to their high yield and ultimate tensile strengths, enabling the production of lighter car body structures while meeting safety standards. However, they have some setbacks compared to conventional steels, such as edge cracking through sheet thickness caused by forming components with shear-cut edges. When characterizing the formability of sheet metal materials, the hole expansion test is an industry-standard method used to evaluate the stretch-flangeability of their edges. However, accurately visualizing the first cracking is usually tricky and may be subjective, often leading to inconsistent results and low reproducibility with some impact of the operator on both direct and post-processing measurements. To address these issues, a novel digital image processing method is presented to reduce operator reliance and enhance the accuracy and efficiency of the hole expansion test results. By leveraging advanced image processing algorithms, the proposed approach detects the appearance of the first edge cracks, enabling a more precise determination of the hole expansion ratio (HER). Furthermore, it provides valuable insights into the evolution of the hole diameter, allowing for a comprehensive understanding of the material behavior during the test. The proposed method was evaluated for different materials, and the corresponding HER values were compared with the traditional method. Full article
(This article belongs to the Special Issue Computer Methods in Metallic Materials (2nd Edition))
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33 pages, 2382 KiB  
Article
Algorithms to Estimate the Ductile to Brittle Transition Temperature, Upper Shelf Energy, and Their Uncertainties for Steel Using Charpy V-Notch Shear Area and Absorbed Energy Data
by Nathaniel T. Switzner, Joel Anderson, Lanya Ali Ahmed, Michael Rosenfeld and Peter Veloo
Metals 2023, 13(5), 877; https://doi.org/10.3390/met13050877 - 2 May 2023
Cited by 1 | Viewed by 2877
Abstract
Toughness and the transition from ductile to brittle behavior are long-standing concerns for applications of ferritic steel such as line-pipe. Three algorithms have been developed to fit a hyperbolic tangent curve to any Charpy V-notch dataset and estimate the uncertainty for (1) the [...] Read more.
Toughness and the transition from ductile to brittle behavior are long-standing concerns for applications of ferritic steel such as line-pipe. Three algorithms have been developed to fit a hyperbolic tangent curve to any Charpy V-notch dataset and estimate the uncertainty for (1) the 85% shear appearance area transition temperature and (2) the upper shelf absorbed energy. To fit the hyperbolic tangent curve to the data the (I) first algorithm relied on iterative estimation of four-parameters; (II) the second algorithm on two parameters (after simplification based on physical assumptions); and (III) the third algorithm on only one parameter (after further simplification). The algorithms were written using the open-source programing language, R. The minimum input requirements for the algorithm are experimental data for shear appearance area and absorbed energy from at least four temperatures for the four-parameter algorithm, two temperatures for the two-parameter algorithm, and one temperature for the one-parameter algorithm. The test temperatures and quantity of tests at each temperature can vary. The algorithms are described in detail and demonstrated using a data set of 12 Charpy test results (shear area and absorbed energy) from one API-5L grade X52 pipe with 4.5 mm thick Charpy bars. A future paper will test and compare the algorithms using a wide variety of Charpy V-notch data sets to clarify their applicability and possible limitations. Full article
(This article belongs to the Special Issue Computer Methods in Metallic Materials (2nd Edition))
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