Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.4
3.2
Journals
Materials
Special Issues
Microstructural and Mechanical Characteristics of Welded Joints
materials-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Materials Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Microstructural and Mechanical Characteristics of Welded Joints

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 78

Special Issue Editors

Dr. Jie Wu

E-Mail Website
Guest Editor
College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: electronic packaging; solder joint; reliability; interfacial metallurgy
Dr. Guoqiang Huang

E-Mail Website
Guest Editor
College of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Yudao Street 29, Nanjing 210016, China
Interests: friction stir welding/processing; dissimilar metal joining; additive manufacturing

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest research and developments in the field of electronic packaging, with a particular focus on solder joints, reliability, and interfacial metallurgy. It emphasizes friction stir welding/processing, dissimilar metal joining, and additive manufacturing. It seeks to provide a comprehensive platform for researchers and engineers to share their findings and insights on these aspects. This Special Issue covers both theory and practice, aiming to deepen understanding, promote innovation, and drive interdisciplinary breakthroughs that connect basic research with industrial application.

Dr. Jie Wu
Dr. Guoqiang Huang
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. 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

  • electronic packaging
  • solder joint
  • reliability
  • interfacial metallurgy
  • friction stir welding/processing
  • dissimilar metal joining
  • additive manufacturing
  • microstructure
  • properties

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

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

21 pages, 4943 KiB  
Article
Machine Learning-Based Fatigue Life Prediction for E36 Steel Welded Joints
by Lina Zhu, Hongye Guo, Zongxian Song, Yong Liu, Jinling Peng and Jifeng Wang
Materials 2025, 18(15), 3481; https://doi.org/10.3390/ma18153481 - 24 Jul 2025
Abstract
E36 steel, widely used in shipbuilding and offshore structures, offers moderate strength and excellent low-temperature toughness. However, its welded joints are highly susceptible to fatigue failure. Cracks typically initiate at weld toes or within the heat-affected zone (HAZ), severely limiting the fatigue life [...] Read more.
E36 steel, widely used in shipbuilding and offshore structures, offers moderate strength and excellent low-temperature toughness. However, its welded joints are highly susceptible to fatigue failure. Cracks typically initiate at weld toes or within the heat-affected zone (HAZ), severely limiting the fatigue life of fabricated components. Traditional life prediction methods are complex, inefficient, and lack accuracy. This study proposes a machine learning (ML) framework for efficient fatigue life prediction of E36 welded joints. Welded specimens using SQJ501 filler wire on prepared E36 steel established a dataset from 23 original fatigue test data points. The dataset was expanded via Z-parameter model fitting, with data scarcity addressed using SMOTE. Pearson correlation analysis validated data relationships. After grid-optimized training on the augmented data, models were evaluated on the original dataset. Results demonstrate that the machine learning models significantly outperformed the Z-parameter formula (R2 = 0.643, MAPE = 16.15%). The artificial neural network (R2 = 0.972, MAPE = 4.45%) delivered the best overall performance, while the random forest model exhibited high consistency between validation (R2 = 0.888, MAPE = 6.34%) and testing sets (R2 = 0.897), with its error being significantly lower than that of support vector regression. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Characteristics of Welded Joints)
►▼ 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