Optimization of Metal Additive Manufacturing Processes (Volume 2)

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Additive Manufacturing".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 1084

Special Issue Editor


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Guest Editor
School of Sustainable Engineering and the Built Environment, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85281, USA
Interests: additive manufacturing of metals, especially powder bed melting processes including leaser melting processes and electron beam melting processes; computational and experimental approaches for the optimization of powder bed processes
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Special Issue Information

Dear Colleagues,

Additive manufacturing (AM), also known as 3D printing, utilizes advanced computer algorithms and sophisticated machines to deposit materials layer by layer to form a part. The AM technique is a disruptive technology that has revolutionized manufacturing due to its many advantages, such as its low-cost and rapid prototyping, reduced waste of materials, lack of geometric limitations, freedom in design, and ability to fabricate complex and customized parts, improved product performance, and enhanced material efficiency. However, achieving high product quality and the desired properties and geometries of additively manufactured components is dependent on many different parameters, such as process parameters (i.e., alloy composition, process parameters, and geometry), and is still the common topic of research papers.

This Special Issue aims to present the state-of-the-art achievements in the field of additive manufacturing and its related topics. Papers on experimental work, numerical simulation, or a combination of both are welcome. The specific scopes of interest include but are not limited to:

  • Process optimization for reducing defects;
  • Approaches in reducing the residual stresses in parts made using AM;
  • Design optimization and concurrent design;
  • Microstructural manipulation and optimization;
  • In situ monitoring of the process including measurements of temperatures, stress, defects, geometry, etc.;
  • Use of machine learning and artificial intelligence in process and use of computational and experimental learning approaches;
  • New materials and processes;
  • Alloy development for AM;
  • New AM technologies;
  • Microstructural/mechanical characterization techniques;
  • Tribology, tribocorrosion, oxidation, and corrosion properties;
  • Simulation and modeling of AM processes;
  • New applications.

Prof. Dr. Jafar Razmi
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. Metals 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 2600 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

  • additive manufacturing (AM)
  • alloy development
  • processing
  • advanced materials
  • characterization
  • machine learning
  • artificial intelligence

Published Papers (1 paper)

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Research

14 pages, 8501 KiB  
Article
Advancing Wire Arc Directed Energy Deposition: Analyzing Impact of Materials and Parameters on Bead Shape
by Stephen Price, Kiran Judd, Matthew Gleason, Kyle Tsaknopoulos, Danielle L. Cote and Rodica Neamtu
Metals 2024, 14(3), 282; https://doi.org/10.3390/met14030282 - 28 Feb 2024
Viewed by 784
Abstract
This study advances foundational knowledge regarding the impact of processing parameters and material selection on bead shape in Wire Arc directed energy deposition (Wire Arc DED) additive manufacturing. Through the collection and analysis of the largest Wire Arc DED bead shape dataset to [...] Read more.
This study advances foundational knowledge regarding the impact of processing parameters and material selection on bead shape in Wire Arc directed energy deposition (Wire Arc DED) additive manufacturing. Through the collection and analysis of the largest Wire Arc DED bead shape dataset to date, this work confirms the dominant roles of the feed rate and travel speed on bead shape. Specifically, an increasing feed rate correlates with an increased bead size, while increasing the travel speed decreases the bead size. Furthermore, as the first dataset to directly compare bead shape across different wire–substrate combinations, this research identified that material selection has a smaller, but still relevant, impact on bead shape compared to the feed rate and travel speed. These insights into the roles of parameters and materials are critical for improving large-scale manufacturing efficiency and quality with Wire Arc DED. By generating a robust, multi-material dataset, this work enables applications of machine learning to optimize Wire Arc DED through quicker manufacturing, reduced material waste, and improved structural integrity. Full article
(This article belongs to the Special Issue Optimization of Metal Additive Manufacturing Processes (Volume 2))
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