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Novel Materials for Additive Manufacturing

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 672

Special Issue Editor


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Guest Editor
Department of Physics, Campus Montilivi s/n, University of Girona, 17003 Girona, Spain
Interests: powder metallurgy; structural analysis; thermal analysis; mechanical alloying; nanocrystalline
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to additive manufacturing, also known as 3D printing. The additive manufacturing process usually involves the fabrication of three-dimensional parts directly from CAD models by adding materials layer by layer. Thus, it is able to build components with complex geometry. It is being applied in the processing of all types of materials, whether traditional (ceramics, metals, polymers, composites), biological or food-related. We invite authors to submit articles concerning the applications of different additive manufacturing techniques, such as stereolithography, digital light processing, selective laser sintering/melting, electron beam melting, fusion deposition modeling, multijet/polyjet 3D printing, direct injection writing or laminated object manufacturing. Articles investigating additive manufacturing process simulations, the design of new equipment, reviews about specific techniques or materials, and/or the optimization of processing conditions are also welcome. 

Prof. Dr. Joan-Josep Suñol
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. Materials 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 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
  • fabrication additive
  • 3D printing
  • rapid prototyping
  • fused deposition modeling
  • stereolithography
  • selective laser sintering/melting
  • direct injection writing

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Published Papers (1 paper)

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Research

22 pages, 8502 KiB  
Article
Design of Novel Auxetic Bi-Materials Using Convolutional Neural Networks
by Iulian Constantin Coropețchi, Dan Mihai Constantinescu, Alexandru Vasile, Andrei Ioan Indreș and Ștefan Sorohan
Materials 2025, 18(8), 1772; https://doi.org/10.3390/ma18081772 - 13 Apr 2025
Viewed by 172
Abstract
A convolutional neural network (CNN) was developed to predict the Poisson’s ratio of representative volume elements (RVEs) composed of a bi-material system with soft and hard phases. The CNN was trained on a dataset of binary microstructure configurations, learning to approximate the effective [...] Read more.
A convolutional neural network (CNN) was developed to predict the Poisson’s ratio of representative volume elements (RVEs) composed of a bi-material system with soft and hard phases. The CNN was trained on a dataset of binary microstructure configurations, learning to approximate the effective Poisson’s ratio based on spatial material distribution. Once trained, the network was integrated into a greedy optimization algorithm to identify microstructures with auxetic behavior. The algorithm iteratively modified material arrangements, leveraging the CNN’s rapid inference to explore and refine configurations efficiently. The results demonstrate the feasibility of using deep learning for microstructure evaluation and optimization, offering a computationally efficient alternative to traditional finite element simulations. This approach provides a promising tool for the design of advanced metamaterials with tailored mechanical properties. Full article
(This article belongs to the Special Issue Novel Materials for Additive Manufacturing)
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