Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
3.4
Journals
Materials
Special Issues
Forming Process and Mechanical Behavior Analysis of Light Metals and...
materials-logo
Submit to Materials Review for Materials Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Forming Process and Mechanical Behavior Analysis of Light Metals and Alloys

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

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 1360

Special Issue Editor

Dr. Jianlei Yang

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China
Interests: lightweight alloys; superplastic forming; electrically assisted forming; microstructure evolution; mechanical properties

Special Issue Information

Dear Colleagues,

As a critical class of structural materials, lightweight alloys such as Ti, Al and Mg alloys and their metal matrix composites have received increasing attention in the fields of aerospace and automobiles. Plastic forming is an essential step toward application. However, with the strong demand for lightweight properties, forming technologies are also facing two important challenges: (i) the shape is becoming increasingly more complex; and (ii) the performance requirements of the component are becoming higher and higher. Hence, it is important to develop a more specific and dedicated forming technology and reveal the underlying relationships between the forming process and microstructure evolution, as well as the application performance.

Therefore, the objective of this Special Issue is to publish full research papers of original, significant, and rigorous work and contribute to guide actual product manufacturing and improved component performance. Suitable topics include but are not limited to the following:

  • Development of the traditional forming technique, such as stamping/forging/ extruding/ bulging, etc.;
  • New forming technique, such as energy-field-assisted forming/incremental forming/additive manufacturing, etc.;
  • Simulation with experimental verification;
  • Microstructure evolution and performance evaluation.

Dr. Jianlei Yang
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. 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

  • lightweight metals
  • forming technology
  • numerical simulation
  • microstructural evolution
  • mechanical properties

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 8119 KiB  
Article
Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach
by Aleksandra Thamm, Florian Thamm, Annette Sawodny, Sally Zeitler, Marion Merklein and Andreas Maier
Materials 2023, 16(21), 7001; https://doi.org/10.3390/ma16217001 - 01 Nov 2023
Viewed by 1058
Abstract
An accurate description of the formability and failure behavior of sheet metal materials is essential for an optimal forming process design. In this respect, the forming limit curve (FLC) based on the Nakajima test, which is determined in accordance with DIN EN ISO [...] Read more.
An accurate description of the formability and failure behavior of sheet metal materials is essential for an optimal forming process design. In this respect, the forming limit curve (FLC) based on the Nakajima test, which is determined in accordance with DIN EN ISO 12004-2, is a wide-spread procedure for evaluating the formability of sheet metal materials. Thereby the FLC is affected by influences originating from intrinsic factors of the Nakajima test-setup, such as friction, which leads to deviations from the linear strain path, biaxial prestress and bending superposition. These disadvantages can be circumvented by an alternative test combination of uniaxial tensile test and hydraulic bulge test. In addition, the forming limit capacity of many lightweight materials is underestimated using the cross-section method according to DIN EN ISO 12004-2, due to the material-dependent occurrence of multiple strain maxima during forming or sudden cracking without prior necking. In this regard, machine learning approaches have a high potential for a more accurate determination of the forming limit curve due to the inclusion of other parameters influencing formability. This work presents a machine learning approach focused on uniaxial tensile tests to define the forming limit of lightweight materials and high-strength steels. The transferability of an existing weakly supervised convolutional neural network (CNN) approach was examined, originally designed for Nakajima tests, to uniaxial tensile tests. Additionally, a stereo camera-based method for this purpose was developed. In our evaluation, we train and test materials, including AA6016, DX54D, and DP800, through iterative data composition, using cross-validation. In the context of our stereo camera-based approach, strains for different materials and thicknesses were predicted. In this cases, our method successfully predicted the major strains with close agreement to ISO standards. For DX54D, with a thickness of 0.8 mm, the prediction was 0.659 (compared to ISO’s 0.664). Similarly, for DX54D, 2.0 mm thickness, the predicted major strain was 0.780 (compared to ISO 0.705), and for AA6016, at 1.0 mm thickness, a major strain of 0.314 (in line with ISO 0.309) was estimated. However, for DP800 with a thickness of 1.0 mm, the prediction yielded a major strain of 0.478 (as compared to ISO 0.289), indicating a divergence from the ISO standard in this particular case. These results in general, generated with the CNN stereo camera-based approach, underline the quantitative alignment of the approach with the cross-section method. Full article
(This article belongs to the Special Issue Forming Process and Mechanical Behavior Analysis of Light Metals and Alloys)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop