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Advances in Polymers Additive Manufacturing

A Special Issue of Polymers (ISSN 2073-4360) belonging to the section "Polymer Processing and Engineering".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 852

Editors


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Guest Editor
Department of Materials Science and Testing, Westphalian University of Applied Sciences Gelsenkirchen Bocholt Recklinghausen, Neidenburgerstr. 43, 45897 Gelsenkirchen, Germany
Interests: corrosion; surface engineering; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Engineering Science, Faculty of Engineering, Babeș-Bolyai University, 320085 Reșița, Romania
Interests: materials science; surface engineering; additive manufacturing; failure analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

We are pleased to invite submissions to the Special Issue “Advances in Polymer Additive Manufacturing.” Polymer-based additive manufacturing continues to evolve rapidly, driven by breakthroughs in materials, process engineering, and application-specific design. This Special Issue seeks high‑quality original research articles, reviews, and communications that explore recent developments in high‑performance and sustainable polymers, multi‑material and functional printing, polymer bioprinting, computational- and AI‑assisted optimization, and innovations that enhance structure–property–processing relationships, recyclability, and scalability.

We welcome interdisciplinary contributions from materials science, polymer chemistry, engineering, and applied technologies. Studies that address emerging industrial, medical, or environmental applications are particularly encouraged.

Topics of interest include (but are not limited to) the following:

  • Novel polymer materials for AM;
  • High‑performance, bio‑based, and sustainable polymers;
  • Multi‑material and functional printing strategies;
  • Polymer bioprinting and biomedical applications;
  • Advanced characterization and structure–property analysis;
  • AI‑ and simulation‑assisted design and process optimization;
  • Recyclability, circular economy approaches, and waste reduction;
  • Industrial-scale polymer AM technologies and applications.

Dr. Gabriela Mǎrginean
Prof. Dr. Doina Frunzăverde
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers 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 2700 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

  • polymer additive manufacturing
  • high performance polymers
  • polymer composites
  • multi material printing
  • sustainable materials
  • bioprinting
  • process optimization
  • AI in AM
  • structure–property relationships
  • recyclability
  • advanced applications.

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Published Papers (1 paper)

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Research

19 pages, 4487 KB  
Article
A Heterogeneous Multi-Output Stacked Learning Framework for Mechanical Property Prediction of FDM-Printed ASA: Experimental Validation
by Afnan Haider Khan, Farheen Umar, Umar Ayoub, Mushaf Ur Rehman Khan, Shahbaz Haneef and Muhammad Farooq Siddique
Polymers 2026, 18(17), 2100; https://doi.org/10.3390/polym18172100 - 29 Aug 2026
Viewed by 506
Abstract
Accurate prediction of the mechanical performance of polymer components fabricated by fused deposition modelling (FDM) remains challenging owing to the complex nonlinear relationships between process parameters and material properties, limiting reliable process planning and broader industrial adoption of polymer additive manufacturing. This study [...] Read more.
Accurate prediction of the mechanical performance of polymer components fabricated by fused deposition modelling (FDM) remains challenging owing to the complex nonlinear relationships between process parameters and material properties, limiting reliable process planning and broader industrial adoption of polymer additive manufacturing. This study develops and experimentally validates a heterogeneous multi-output stacked ensemble learning framework for the simultaneous prediction of tensile strength, flexural strength, compressive strength, Rockwell hardness, and Charpy impact strength of acrylonitrile styrene acrylate (ASA), a high-performance engineering thermoplastic with excellent weatherability and ultraviolet resistance that remains comparatively underexplored in data-driven FDM research. A Definitive Screening Design (DSD) was employed to investigate eight critical process parameters: extrusion temperature (ET), bed temperature (BT), infill density (ID), layer height (LH), print speed (PS), raster angle (RA), build orientation (BO), and cooling fan speed (CFS). Multiple supervised learning algorithms were systematically benchmarked, and the highest-performing complementary models were integrated into a heterogeneous stacked ensemble for simultaneous multi-output prediction. The proposed framework achieved an overall R2 of 0.9943 with an overall RMSE of 0.9758, while the individual prediction models attained R2 values ranging from 0.9898 to 0.9967. Beyond improving predictive accuracy, the proposed AI-assisted framework provides a data-driven basis for mechanical-property prediction and establishes a surrogate modelling framework that may subsequently be coupled with dedicated optimization or decision-making methods. Full article
(This article belongs to the Special Issue Advances in Polymers Additive Manufacturing)
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