Intelligent and Sustainable Welding: State of the Art, Challenges and Prospects

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 350

Special Issue Editors


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Guest Editor
School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: arc welding & additive manufacturing; robotic & intelligent welding; welding process control and automation

E-Mail Website
Guest Editor
Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, China
Interests: laser welding; laser welding of dissimilar materials; numerical simulation of welding process; welding defect detection
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Special Issue Information

Dear Colleagues,

Intelligent and sustainable welding technology is at the forefront of the current welding field. It integrates modern control theory, artificial intelligence, material science, and environmental science to improve the efficiency, accuracy, and environmental sustainability of the welding process, particularly in metal fabrication. The ability to optimize welding processes for different metals, such as high-strength steels, aluminum alloys, and titanium, is key to advancing industries like the aerospace, automotive, and manufacturing industries.

Intelligent welding incorporates advanced technologies such as robotics, artificial intelligence, and machine learning algorithms to enhance the precision and quality of metal welding. By considering the unique properties of various metals, such as their thermal conductivity and mechanical strength, intelligent welding systems can adapt in real time to ensure optimal results. Sustainability is also a crucial aspect of intelligent welding. It aims to minimize the environmental impact of welding operations by reducing energy consumption, waste generation, and emissions, particularly when working with energy-intensive metals like steel and aluminum.

This Special Issue aims to collate recent research studies on intelligent welding systems (such as power supplies, robotic systems, and control systems), multi-information fusion sensing technologies (including visual, acoustic, electrical, spectral, optical, infrared, etc.), advanced welding quality control algorithms (weld seam tracking, in-process welding quality monitoring, welding defect detection, etc.), teaching-free programming technologies (offline programming, adaptive path planning and tracking, the online adjustment of welding parameters, etc.), welding IoT, green welding methods, and welding consumables, with a focus on metals.

We also welcome reviews and papers on new processing technologies used to manufacture and join metallic materials, including steels, aluminum alloys, titanium alloys, and other advanced metals. 

Dr. Qiang Wang
Dr. Yanxi Zhang
Guest Editors

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Keywords

  • intelligent robotic welding
  • artificial intelligence
  • multi-information fusion
  • weld seam tracking
  • defect detection
  • path planning
  • green welding
  • welding Internet of Things
  • metal joining
  • advanced welding techniques

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Published Papers (1 paper)

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Research

28 pages, 24038 KiB  
Article
Advanced Porosity Control of CP780 Galvanized Steel During Gas Metal Arc Welding with Pulsed Arc
by Carlos Adrián García Ochoa, Jorge Alejandro Verduzco Martínez, Francisco Fernando Curiel-López, Víctor Hugo López-Morelos, José Jaime Taha-Tijerina, Ariosto Medina Flores and Maleni García Gómez
Metals 2025, 15(5), 513; https://doi.org/10.3390/met15050513 - 1 May 2025
Viewed by 128
Abstract
This study investigated the control of porosity during gas metal arc welding with pulsed arc (GMAW-P) of complex-phase 780 (CP780) galvanized steel. Due to the Zn coating on this type of steel, porosity forms during welding as a result of Zn vaporization. The [...] Read more.
This study investigated the control of porosity during gas metal arc welding with pulsed arc (GMAW-P) of complex-phase 780 (CP780) galvanized steel. Due to the Zn coating on this type of steel, porosity forms during welding as a result of Zn vaporization. The objective was to optimize the welding parameters to minimize porosity with a design of experiments using an L9 orthogonal array to analyze the effects of peak current (Ip), pulse time (tp), and pulse frequency (f) in high-speed welding conditions. The results showed that porosity was significantly reduced with a peak current of 313 A, a frequency of 10 Hz, and a pulse time of 10 ms, achieving ~0% porosity in the validation welding trials. A microstructural analysis identified allotriomorphic ferrite, Widmanstätten ferrite, acicular ferrite, bainite, and martensite in the heat-affected zone (HAZ). A predictive model to anticipate the percentage of porosity with an R2 of 99.97% was developed, and an ANOVA determined the peak current as the most critical factor in porosity formation. Full article
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