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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


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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

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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

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Keywords

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

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Published Papers (1 paper)

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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)
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