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JMMP
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Advances in Directed Energy Deposition Additive Manufacturing
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Advances in Directed Energy Deposition Additive Manufacturing

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 668

Special Issue Editors

Dr. Zhichao Liu

E-Mail Website
Guest Editor
Department of Industrial and Management Systems Engineering, West Virginia University, 1306 Evansdale Drive, Morgantown, WV 26506, USA.
Interests: additive manufacturing; material science; sustainable manufacturing
Dr. Yingbin Hu

E-Mail Website
Guest Editor
Department of Mechanical and Manufacturing Engineering, Miami University, 650 E High St. Oxford, OH 45056, USA
Interests: additive manufacturing; 4D printing; acoustic field-assisted AM
Special Issues, Collections and Topics in MDPI journals
Dr. Dazhong Wu

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of Central Florida, 12760 Pegasus Drive, Orlando, FL 32816, USA
Interests: smart manufacturing; additive manufacturing; engineering design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Directed energy deposition (DED) additive manufacturing (AM) has been recognized as an efficient and sustainable technology in advanced manufacturing. Over the past few years, considerable discussion has been made to promote DED AM for better performance in manufacturing. The discussions focus on basic theoretical research, process optimization and control, technology innovation and industrial applications. Although DED technology is growing rapidly worldwide, many scientific and technical challenges need attention to make this technology platform more versatile. The challenges include complex phase transformations and microstructural changes, non-uniform residual stresses and distortions, porosity, lack of fusion and cracking, etc.

In this Special Issue of JMMP, we are looking for recent advances in DED technology, including material development, process design and optimization, physical characteristics, defects, challenges and applications. We are interested in contributions that focus on topics such as:

  • Laser–material interaction mechanisms;
  • Melt pool thermal behavior modeling and simulation;
  • Process optimization, in situ process monitoring and feedback control;
  • Mechanical characteristics and behaviors;
  • Defect formation mechanisms and characterization;
  • DED-based hybrid additive manufacturing.

Dr. Zhichao Liu
Dr. Yingbin Hu
Dr. Dazhong Wu
Guest Editors

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 semimonthly 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

  • directed energy deposition
  • process optimization
  • characterization
  • hybrid manufacturing
  • interaction mechanisms

Published Papers (1 paper)

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Research

14 pages, 4717 KiB  
Article
Exploring Multi-Armed Bandit (MAB) as an AI Tool for Optimising GMA-WAAM Path Planning
by Rafael Pereira Ferreira, Emil Schubert and Américo Scotti
J. Manuf. Mater. Process. 2024, 8(3), 99; https://doi.org/10.3390/jmmp8030099 - 15 May 2024
Viewed by 381
Abstract
Conventional path-planning strategies for GMA-WAAM may encounter challenges related to geometrical features when printing complex-shaped builds. One alternative to mitigate geometry-related flaws is to use algorithms that optimise trajectory choices—for instance, using heuristics to find the most efficient trajectory. The algorithm can assess [...] Read more.
Conventional path-planning strategies for GMA-WAAM may encounter challenges related to geometrical features when printing complex-shaped builds. One alternative to mitigate geometry-related flaws is to use algorithms that optimise trajectory choices—for instance, using heuristics to find the most efficient trajectory. The algorithm can assess several trajectory strategies, such as contour, zigzag, raster, and even space-filling, to search for the best strategy according to the case. However, handling complex geometries by this means poses computational efficiency concerns. This research aimed to explore the potential of machine learning techniques as a solution to increase the computational efficiency of such algorithms. First, reinforcement learning (RL) concepts are introduced and compared with supervised machining learning concepts. The Multi-Armed Bandit (MAB) problem is explained and justified as a choice within the RL techniques. As a case study, a space-filling strategy was chosen to have this machining learning optimisation artifice in its algorithm for GMA-AM printing. Computational and experimental validations were conducted, demonstrating that adding MAB in the algorithm helped to achieve shorter trajectories, using fewer iterations than the original algorithm, potentially reducing printing time. These findings position the RL techniques, particularly MAB, as a promising machining learning solution to address setbacks in the space-filling strategy applied. Full article
(This article belongs to the Special Issue Advances in Directed Energy Deposition Additive Manufacturing)
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