Welding and Fatigue of Metallic Materials

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Welding and Joining".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 1461

Special Issue Editor


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Guest Editor
Strength Laboratory, SVÚM, a.s., Čelákovice, Prague-East District, Čelákovice, Czech Republic
Interests: fatigue life; fatigue cracks; fracture mechanics; mechanical properties; microstructure; defects; residual stresses; crack closure
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Special Issue Information

Dear Colleagues,

The welding of metals is a technological process of joining metal parts, components, and structures that have already been widely used for many years. This technology makes it possible to melt materials together using high temperatures and allow them to cool, causing fusion. Though numerous welding technologies are already well known, new advanced methods like laser, electron beam, and others have been recently used. An advantage of welding is its relative easiness and possibility to be performed under different conditions from production assembly lines in factories and repairs in service to the joining of structural components in the field. A disadvantage of welding is that the welded area, typically consisting of a weld metal, fusion zone and heat-affected zone, is characterized by a substantially different microstructure, and different mechanical properties and fatigue resistance behavour in comparison with the original basic material. In addition, welds may contain crack-like or other types of defects, and the cooling process is usually associated with the occurrence of residual stresses. Though residual stresses represent local static prestress and do not affect the stress amplitude, they affect fatigue crack closure, playing an important role in crack-like weld defects.

Welded metal components and structures are often subjected to cyclic fatigue loading. When the fatigue stress amplitude exceeds the overall damage threshold, the damage process occurs. The fatigue damage mechanism is quite complicated and typically has three phases – crack initiation, crack growth, and final failure. Fatigue crack initiation is a local process that occurs in the weakest area of components or structures. Welds, sometimes called structural notches, represent one of such weak areas. For the mentioned reasons, understanding the fatigue resistance of welds to fatigue loading and evaluation of residual life are essential requirements, along with extensive testing. Articles from the mentioned field, but not limited to it, are welcome for this Special Issue.

Dr. Ivo Černý
Guest Editor

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Keywords

  • welding
  • metallic materials
  • microstructure
  • fatigue resistance
  • crack initiation
  • residual stresses
  • weld defects

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Published Papers (1 paper)

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Research

16 pages, 5538 KiB  
Article
Vision-Based Acquisition Model for Molten Pool and Weld-Bead Profile in Gas Metal Arc Welding
by Gwang-Gook Kim, Dong-Yoon Kim and Jiyoung Yu
Metals 2024, 14(12), 1413; https://doi.org/10.3390/met14121413 - 10 Dec 2024
Viewed by 994
Abstract
Gas metal arc welding (GMAW) is widely used for its productivity and ease of automation across various industries. However, certain tasks in shipbuilding and heavy industry still require manual welding, where quality depends heavily on operator skill. Defects in manual welding often necessitate [...] Read more.
Gas metal arc welding (GMAW) is widely used for its productivity and ease of automation across various industries. However, certain tasks in shipbuilding and heavy industry still require manual welding, where quality depends heavily on operator skill. Defects in manual welding often necessitate costly rework, reducing productivity. Vision sensing has become essential in automated welding, capturing dynamic changes in the molten pool and arc length for real-time defect insights. Laser vision sensors are particularly valuable for their high-precision bead profile data; however, most current models require offline inspection, limiting real-time application. This study proposes a deep learning-based system for the real-time monitoring of both the molten pool and weld-bead profile during GMAW. The system integrates an optimized optical design to reduce arc light interference, enabling the continuous acquisition of both molten pool images and 3D bead profiles. Experimental results demonstrate that the molten pool classification models achieved accuracies of 99.76% with ResNet50 and 99.02% with MobileNetV4, fulfilling real-time requirements with inference times of 6.53 ms and 9.06 ms, respectively. By combining 2D and 3D data through a semantic segmentation algorithm, the system enables the accurate, real-time extraction of weld-bead geometry, offering comprehensive weld quality monitoring that satisfies the performance demands of real-time industrial applications. Full article
(This article belongs to the Special Issue Welding and Fatigue of Metallic Materials)
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