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Metals
Special Issues
Plastic Forming, Microstructure, and Property Optimization of Metals (Volume II)
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Plastic Forming, Microstructure, and Property Optimization of Metals (Volume II)

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Casting, Forming and Heat Treatment".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 1762

Special Issue Editor

Dr. Xuefeng Tang

E-Mail Website
Guest Editor
Associate Professor, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: intelligent plastic forming; crystal plasticity; microforming; on-line process monitor and decision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced metals and metallic components play a critical role in modern industries. The development of new metallic materials and novel forming technologies, as well as the optimization of formability and properties, have attracted a lot of interest in the past few decades. As new metals emerge constantly, fundamental knowledge regarding the microstructural evolution mechanism and property control method during material preparation and the forming process are in dire need of significant advances to meet the increasing performance requirements of high-end components.

This Special Issue aims to publish papers that focus on microstructural and property optimization in the preparation and plastic forming of aluminum alloys, titanium alloys, magnesium alloys, superalloys, high-entropy alloys, and their composites. The development of a novel plastic forming process is also welcomed. Innovations in physical-based and data-driven methods for modeling and optimizing the forming process, microstructure, and properties are strongly encouraged.

Dr. Xuefeng Tang
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

  • plastic forming
  • microstructural evolution
  • property optimization
  • deformation behavior
  • modeling
  • metallic materials

Published Papers (2 papers)

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Research

18 pages, 3756 KiB  
Article
Advanced FEM Insights into Pressure-Assisted Warm Single-Point Incremental Forming of Ti-6Al-4V Titanium Alloy Sheet Metal
by Tomasz Trzepieciński, Marcin Szpunar, Robert Ostrowski, Waldemar Ziaja and Maciej Motyka
Metals 2024, 14(6), 619; https://doi.org/10.3390/met14060619 - 24 May 2024
Viewed by 270
Abstract
This study employs the finite element (FE) method to analyze the Incremental Sheet Forming (ISF) process of Ti-6Al-4V titanium alloy. The numerical modeling of pressure-assisted warm forming of Ti-6Al-4V sheets with combined oil-heating and friction stir rotation-assisted heating of the workpiece is presented [...] Read more.
This study employs the finite element (FE) method to analyze the Incremental Sheet Forming (ISF) process of Ti-6Al-4V titanium alloy. The numerical modeling of pressure-assisted warm forming of Ti-6Al-4V sheets with combined oil-heating and friction stir rotation-assisted heating of the workpiece is presented in this article. The thermo-mechanical FE-based numerical model took into account the characteristics of the mechanical properties of the sheet along with the temperature. The experimental conditions were replicated in FEM simulations conducted in Abaqus/Explicit, which incorporated boundary conditions and evaluated various mesh sizes for enhanced accuracy and efficiency. The simulation outcomes were compared with actual experimental results to validate the FE-based model’s predictive capacity. The maximum temperature of the tool measured using infrared camera was approximately 326 °C. Different mesh sizes were considered. The results of FEM modeling were experimentally validated based on axial forming force and thickness distribution measured using the ARGUS optical measuring system for non-contact acquisition of deformations. The greatest agreement between FEM results and the experimental result of the axial component of forming force was obtained for finite elements with a size of 1 mm. The maximum values of the axial component of forming force determined experimentally and numerically differ by approximately 8%. The variations of the forming force components and thickness distribution predicted by FEM are in good agreement with experimental measurements. The numerical model overestimated the wall thickness with an error of approximately 5%. By focusing on the heating techniques applied to Ti-6Al-4V titanium alloy sheet, this comparative analysis underlines the adaptability and precision of numerical analysis applied in modeling advanced manufacturing processes. Full article
(This article belongs to the Special Issue Plastic Forming, Microstructure, and Property Optimization of Metals (Volume II))
33 pages, 9002 KiB  
Article
Recurrent Neural Networks and Three-Point Bending Test on the Identification of Material Hardening Parameters
by Daniel J. Cruz, Manuel R. Barbosa, Abel D. Santos, Rui L. Amaral, Jose Cesar de Sa and Jose V. Fernandes
Metals 2024, 14(1), 84; https://doi.org/10.3390/met14010084 - 10 Jan 2024
Cited by 2 | Viewed by 1131
Abstract
The continuous evolution of metallic alloys in the automotive industry has led to the development of more advanced and flexible constitutive models that attempt to accurately describe the various fundamental properties and behavior of these materials. These models have become increasingly complex, incorporating [...] Read more.
The continuous evolution of metallic alloys in the automotive industry has led to the development of more advanced and flexible constitutive models that attempt to accurately describe the various fundamental properties and behavior of these materials. These models have become increasingly complex, incorporating a larger number of parameters that require an accurate calibration procedure to fit the constitutive parameters with experimental data. In this context, machine learning (ML) methodologies have the potential to advance material constitutive modeling, enhancing the efficiency of the material parameter calibration procedure. Recurrent neural networks (RNNs) stand out among various learning algorithms due to their ability to process sequential data and overcome limitations imposed by nonlinearities and multiple parameters involved in phenomenological models. This study explores the modeling capabilities of long short-term memory (LSTM) structures, a type of RNN, in predicting the hardening behavior of a sheet metal material using the results of a standardized experimental three-point bending test, with the aim of extending this methodology to other experimental tests and constitutive models. Additionally, a variable analysis is performed to select the most important variables for this experimental test and assess the influence of friction, material thickness, and elastic and plastic properties on the accuracy of predictions made by neural networks. The required data for designing and training the network solutions are collected from numerical simulations using finite element methodology (FEM), which are subsequently validated by experiments. The results demonstrate that the proposed LSTM-based approach outperforms traditional identification techniques in predicting the material hardening parameters. This suggests that the developed procedure can be effectively applied to efficiently characterize different materials, especially those extensively used in industrial applications, ranging from mild steels to advanced high-strength steels. Full article
(This article belongs to the Special Issue Plastic Forming, Microstructure, and Property Optimization of Metals (Volume II))
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