Arc-Based Directed Energy Deposition: Processes, Applications and Monitoring

Special Issue Editors


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Guest Editor
Center for Research and Development of Welding and Additive Manufacturing Processes, Universidade Federal de Uberlândia, Uberlândia 38408-206, Brazil
Interests: welding and additive manufacturing

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Guest Editor
Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA
Interests: applied machine vision; control systems; manufacturing processes; robotic welding
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Dipartimento di Meccanica, Matematica e Management, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
Interests: FEM (finite element method) process simulation; discrete event systems simulation; additive manufacturing; welding; laser processing; statistical methods; artificial neural networks (ANN); machine Learning analysis and optimization

Special Issue Information

Dear Colleagues,

Arc-Based Direct Energy Deposition (DED-Arc) has garnered growing attention as a promising, efficient, and cost-effective technique for large-scale metal additive manufacturing. Over the past few years, extensive research has been conducted to improve DED-Arc processes, optimize parameters, develop new materials, and implement effective monitoring systems to enhance final part quality and process repeatability.

Despite significant advances, several scientific and technical challenges remain, including controlling complex thermal cycles, minimizing residual stresses and distortions, managing microstructural heterogeneity, and preventing typical defects such as porosity, lack of fusion, and cracking.

This Special Issue of the Journal of Manufacturing and Materials Processing (JMMP), titled "Arc-Based Direct Energy Deposition: Processes, Applications and Monitoring", aims to present recent developments and research trends in DED-Arc. We invite original research articles, reviews, and case studies covering a broad range of topics, including but not limited to:

  • Innovations in DED-Arc processes design and optimization;
  • Process monitoring and control systems (e.g., thermal imaging, sensors, and AI);
  • Influence of shielding gas, wire feedstock, and energy input;
  • Modeling and simulation of DED-Arc deposition behavior;
  • Industrial applications in aerospace, energy, automotive, and marine sectors;
  • Sustainability, cost analysis, and lifecycle assessment of DED-Arc processes.

Dr. Vinicius Lemes Jorge
Prof. Dr. Yuming Zhang
Dr. Nicola Contuzzi
Guest Editors

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Keywords

  • direct energy deposition
  • process monitoring
  • process-structure-property relationships
  • process optimization
  • deposition path strategies

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Published Papers (1 paper)

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Research

22 pages, 4864 KB  
Article
A K-Means Clustering Approach for Accelerated Path Planning in GMA-DED: The Fast Advanced-Pixel Strategy
by Rafael P. Ferreira, Vinicius Lemes Jorge, Emil Schubert and Américo Scotti
J. Manuf. Mater. Process. 2026, 10(2), 55; https://doi.org/10.3390/jmmp10020055 - 5 Feb 2026
Viewed by 755
Abstract
The performance of Gas Metal Arc-Directed Energy Deposition (GMA-DED) strongly depends on efficient path-planning strategies that balance trajectory quality and computational cost. With the purpose of developing a computationally faster and more scalable path-planning approach, this study introduces the Fast Advanced-Pixel strategy by [...] Read more.
The performance of Gas Metal Arc-Directed Energy Deposition (GMA-DED) strongly depends on efficient path-planning strategies that balance trajectory quality and computational cost. With the purpose of developing a computationally faster and more scalable path-planning approach, this study introduces the Fast Advanced-Pixel strategy by integrating the K-means clustering algorithm into to the Advanced Pixel strategy version to reduce the dimensionality of an optimization problem. Computational validation was conducted on four geometrically distinct parts using different clustering configurations. Statistical analysis (ANOVA) was applied to assess the significance of the results. The findings revealed that by increasing the number of clusters, computational time is substantially reduced, achieving up to a twenty-fold improvement compared with the former strategy, while maintaining consistent trajectory quality. Experimental validation using complex parts, such as a “Jaw Gripper” and a “C-frame” of a resistance spot welding gun, confirmed defect-free deposition and dimensional agreement with the CAD models. Accordingly, within the scope of GMA-DED technology and pixel-based path-planning strategies, the Fast Advanced-Pixel approach demonstrates a significant improvement in computational efficiency while preserving trajectory quality, enabling the accurate and reliable fabrication of geometrically complex metallic parts. Full article
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