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Large-Scale Metal Additive Manufacturing
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Large-Scale Metal Additive Manufacturing

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1716

Special Issue Editor

Dr. Hessamoddin Moshayedi

E-Mail Website
Guest Editor
Institute for Steel Construction, Leibniz University Hannover, Hannover, Germany
Interests: welding technologies; residual stresses; digital twins for welding; large-scale metal additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of large-scale metal additive manufacturing (AM) has witnessed remarkable advancements in recent years, driven by the increasing demand for the rapid and cost-effective production of large-scale metal components across various industries, including aerospace, automotive, energy, and construction.

While the AM of large components was initially predominantly used for prototyping, the technology has now expanded its applications to include playing a direct part in production, in a process known as “Rapid Manufacturing”. This transition has been facilitated by advancements in the automation and scalability of metal AM systems, which caused the development of AM setups with high deposition rates and the concept of multi-wire and/or multi-robot printing. However, the reliability and performance of the relatively large components are still unclear. In this regard, and toward the “first time right” printing concept of large-scale metal AM, not only are more component scale performance tests required, but as are efficient and reliable numerical methodologies to predict thermal histories and resulting residual stresses, deformation, and microstructural evolution. These data can then be used to assess the durability and structural integrity of the component. Improvements in controlling and monitoring the process, development, and implementation of digital twins in the process of predicting defects and abnormality, and to prevent them during printing, as well as frameworks for process design and optimisation, printing strategies, and path planning, are some of the other key factors that can impact large-scale metal AM as a cost-effective solution.

This Special Issue of the Journal of Manufacturing and Materials Processing focuses on the recent advancements and ongoing research in large-scale metal additive manufacturing. We invite contributions that explore the latest developments in materials, processes, and applications of this rapidly evolving field.

Topics of interest include, but are not limited to:

  • Efficient numerical solutions for the thermal and thermomechanical simulation of large-scale AM processes;
  • Design and optimisation for the AM of component-scale parts;
  • Process optimisation and path planning of large-scale metal AM;
  • Multi-material printing and functionally graded components;
  • Qualification and performance of additively manufactured parts at a component level;
  • Applications of large-scale metal AM in various industries;
  • Economic and environmental considerations in large-scale additive manufacturing;
  • Structural integrity and lifetime assessment of additively manufactured components .

This Special Issue will provide a valuable platform for researchers, engineers, and practitioners to share their latest findings and insights, fostering further innovation and development in the field of large-scale metal additive manufacturing.

Dr. Hessamoddin Moshayedi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Manufacturing and Materials Processing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • large-scale AM processes
  • additive manufacturing
  • 3D printing
  • metal
  • manufacturing
  • material processing

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Published Papers (2 papers)

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Research

16 pages, 11669 KiB  
Article
Deposition Strategies for Bar Intersections Using Dot-by-Dot Wire and Arc Additive Manufacturing
by Niccolò Grossi, Flavio Lazzeri and Giuseppe Venturini
J. Manuf. Mater. Process. 2025, 9(3), 77; https://doi.org/10.3390/jmmp9030077 - 27 Feb 2025
Cited by 1 | Viewed by 402
Abstract
Dot-by-dot Wire and Arc Additive Manufacturing (WAAM) is a promising technique for producing large-scale lattice structures, offering significant benefits in terms of deposition rate and material utilization. This study explores strategies for fabricating bar intersections using the dot-by-dot WAAM technology, focusing on creating [...] Read more.
Dot-by-dot Wire and Arc Additive Manufacturing (WAAM) is a promising technique for producing large-scale lattice structures, offering significant benefits in terms of deposition rate and material utilization. This study explores strategies for fabricating bar intersections using the dot-by-dot WAAM technology, focusing on creating robust and predictable structures without requiring parameter modifications or real-time monitoring during the deposition. Two different deposition strategies were proposed, that can be, at least geometrically, applied to a general intersection with multiple bars with different angles. In this work such strategies were only experimentally tested on two-bar intersections, assessing their performance in terms of geometrical accuracy, symmetry, and material efficiency. Strategies which utilize layer-by-layer deposition with multiple overlapping dots, called B here, demonstrated the best results in terms of the geometrical features in the intersection zone, assessed by different metrics obtained through an analysis of pictures, such as low asymmetry and high material volume in the intersection zone. In addition, the findings suggest that removing cooling pauses during the deposition of multiple dots on the same layer slightly improves the joint by minimizing excess material buildup. The proposed approach offers a scalable framework for optimizing intersection deposition, paving the way for improved large-scale metal lattice structure manufacturing. Full article
(This article belongs to the Special Issue Large-Scale Metal Additive Manufacturing)
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24 pages, 6584 KiB  
Article
Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing
by Sina Tayebati and Kyu Taek Cho
J. Manuf. Mater. Process. 2025, 9(2), 49; https://doi.org/10.3390/jmmp9020049 - 5 Feb 2025
Viewed by 794
Abstract
Metal additive manufacturing (MAM) has advanced significantly, yet accurately predicting clad characteristics from processing parameters remains challenging due to process complexity and data scarcity. This study introduces a novel hybrid machine learning (ML) framework that integrates validated multi-physics computational fluid dynamics simulations with [...] Read more.
Metal additive manufacturing (MAM) has advanced significantly, yet accurately predicting clad characteristics from processing parameters remains challenging due to process complexity and data scarcity. This study introduces a novel hybrid machine learning (ML) framework that integrates validated multi-physics computational fluid dynamics simulations with experimental data, enabling prediction of clad characteristics unattainable through conventional methods alone. Our approach uniquely incorporates physics-aware features, such as volumetric energy density and linear mass density, enhancing process understanding and model transferability. We comprehensively benchmark ML models across traditional, ensemble, and neural network categories, analyzing their computational complexity through Big O notation and evaluating both classification and regression performance in predicting clad geometries and process maps. The framework demonstrates superior prediction accuracy with sub-second inference latency, overcoming limitations of purely experimental or simulation-based methods. The trained models generate processing maps with 0.95 AUC (Area Under Curve) accuracy that directly guide MAM parameter selection, bridging the gap between theoretical modeling and practical process control. By integrating physics-based simulations with ML techniques and physics-aware features, our approach achieves an R2 of 0.985 for clad geometry prediction and improved generalization over traditional methods, establishing a new standard for MAM process modeling. This research advances both theoretical understanding and practical implementation of MAM processes through a comprehensive, physics-aware machine learning approach. Full article
(This article belongs to the Special Issue Large-Scale Metal Additive Manufacturing)
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