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Microstructural and Mechanical Characteristics of Welded Joints
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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 August 2026 | Viewed by 5069

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

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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 (4 papers)

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Research

23 pages, 2456 KB  
Article
Research on Intelligent Thermal Optimization for Chiplet-Based Heterogeneously Integrated AI Chip Embedded with Leaf-Vein-Inspired Fractal Microchannels
by Jie Wu, Yu Liang, Guibin Liu, Ruiyang Pang, Yi Teng, Chen Li, Xuetian Bao, Shi Lei and Zhikuang Cai
Materials 2026, 19(4), 679; https://doi.org/10.3390/ma19040679 - 10 Feb 2026
Viewed by 1244
Abstract
Conventional cooling schemes that rely on rigid heat-sink-to-die coupling in vertical stacks fail to track the dynamic, non-uniform heat map of high-performance artificial-intelligence (AI) chips employing chiplet-based heterogeneous integration, giving rise to local hot spots. To eliminate this mismatch, we present a leaf-vein-inspired [...] Read more.
Conventional cooling schemes that rely on rigid heat-sink-to-die coupling in vertical stacks fail to track the dynamic, non-uniform heat map of high-performance artificial-intelligence (AI) chips employing chiplet-based heterogeneous integration, giving rise to local hot spots. To eliminate this mismatch, we present a leaf-vein-inspired fractal microchannel tailored for such AI processors. Its hierarchical bifurcation–confluence topology adaptively reshapes the flow field, delivering ultra-low thermal resistance, high heat-transfer coefficients, and uniform dissipation. Coupled with reconfigurable chiplet placement, the design is evaluated through FEM-based orthogonal experiments that rank the influence of coolant, channel diameter/depth, inlet/outlet position, substrate thickness, and flow rate via range analysis and Analysis of Variance (ANOVA). A machine-learned surrogate model of junction temperature is then fed to Particle Swarm Optimization (PSO) for multi-parameter optimization. When re-simulated with the optimal parameter set, the symmetric fractal network lowered the AI chip junction temperature from 127.80 °C to 30.97 °C, a 76% improvement, offering a theoretical basis for hotspot mitigation in advanced heterogeneous AI packages. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Characteristics of Welded Joints)
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12 pages, 1648 KB  
Article
Influence of Niobium Content on the Chemical Composition, Microstructure, and Microhardness of Hardfacing Coatings Applied by SMAW
by Jaime Perez, Jesus Gutierrez, Jhon Olaya, Oscar Piamba and Americo Scotti
Materials 2025, 18(24), 5477; https://doi.org/10.3390/ma18245477 - 5 Dec 2025
Cited by 1 | Viewed by 504
Abstract
This study investigates the chemical composition, microstructural evolution, and mechanical behavior of hardfacing coatings produced by Shielded Metal Arc Welding (SMAW) using electrodes with varying niobium (Nb) contents (0%, 2%, 4%, 6%, and 8%), deposited at a constant current of 120 A and [...] Read more.
This study investigates the chemical composition, microstructural evolution, and mechanical behavior of hardfacing coatings produced by Shielded Metal Arc Welding (SMAW) using electrodes with varying niobium (Nb) contents (0%, 2%, 4%, 6%, and 8%), deposited at a constant current of 120 A and employing two- and three-layer configurations. Optical Emission Spectroscopy (OES) revealed a significant reduction in niobium transfer efficiency, with the Nb content in the coatings reaching up to 3.5 wt%, approximately 50% lower than in the electrodes. Chromium (Cr) content also decreased with increasing Nb additions due to the higher thermochemical affinity of niobium for oxygen, which promotes the formation of Nb oxides during welding. X-ray diffraction (XRD) analyses confirmed the presence of complex carbides, primarily NbC and M7C3-type Cr carbides, embedded in eutectic austenitic matrices. The incorporation of niobium promoted grain refinement and the precipitation of primary NbC carbides, particularly in multilayer coatings where dilution effects were reduced. Scanning Electron Microscopy (SEM) and Energy-Dispersive Spectroscopy (EDS) provided additional evidence, revealing an increased density of NbC particles and a concomitant reduction in CrC particle size with higher Nb contents. Microhardness testing showed a slight increase in hardness with increasing niobium, attributed to the higher intrinsic hardness and finer size of NbC particles. Overall, these findings highlight the role of niobium as an effective grain refiner and hard-phase promoter in SMAW-applied coatings, providing a foundation for optimizing wear-resistant overlays for demanding industrial environments. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Characteristics of Welded Joints)
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24 pages, 10777 KB  
Article
Effect of Laser Shock Peening on High-Cycle Fatigue Performance and Residual Stress in DH36 Welded Joints
by Shengguan Qu, Yulian Sha, Yi Hou, Jianhua Wang, Fenglei Li and Xiaoqiang Li
Materials 2025, 18(22), 5178; https://doi.org/10.3390/ma18225178 - 14 Nov 2025
Cited by 1 | Viewed by 1228
Abstract
DH36 high-strength steel is widely used in shipbuilding and other fields due to its excellent strength, low-temperature toughness, wear resistance, and corrosion resistance. However, the harsh deep-sea environment seriously reduces the service life of welds. In this study we subjected DH36 welded joints [...] Read more.
DH36 high-strength steel is widely used in shipbuilding and other fields due to its excellent strength, low-temperature toughness, wear resistance, and corrosion resistance. However, the harsh deep-sea environment seriously reduces the service life of welds. In this study we subjected DH36 welded joints to laser shock peening at three different energy levels (5 J, 7 J, 9 J) to investigate its effects on microhardness, microstructure, high-cycle fatigue, and residual stress of the DH36 welded joints. Results indicate that LSP can significantly enhance the surface microhardness of welded joints. Notably, the 7 J energy treatment increased the weld zone microhardness from 195 HV0.2 to 231 HV0.2 (18.5% improvement) and the heat-affected zone microhardness from 194 HV0.2 to 234 HV0.2 (20.6% improvement). Residual tensile stress on the specimen surface was offset and replaced by residual compressive stress after LSP. Concurrently, the high-cycle fatigue limit of the specimens was significantly improved, with the most pronounced improvement observed in specimens subjected to 5 J energy—increasing from 258 MPa to 295 MPa, representing an increase of 14.34%. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Characteristics of Welded Joints)
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22 pages, 4943 KB  
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
Cited by 3 | Viewed by 1567
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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