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Polymer Composites: Mechanical Characterization

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Dr. Ali Ercetin

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Guest Editor

Naval Architecture and Marine Engineering, Bandırma Onyedi Eylul University, Bandırma, Türkiye
Interests: manufacturing technologies; composites; characterization; additive manufacturing; mechanical properties

Prof. Dr. Kubilay Aslantaş

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Guest Editor

Department of Mechanical Engineering, Faculty of Technology, University of Afyon Kocatepe, Afyonkarahisar, Türkiye
Interests: modeling; fatigue and fracture; machining; mechanical properties; fatigue
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Special Issue Information

Dear Colleagues,

This Special Issue, titled “Polymer Composites: Mechanical Characterization”, highlights cutting-edge research on the structural performance and failure mechanisms of polymer-based composite materials. With their growing applications in aerospace, automotive, and renewable energy sectors, understanding their mechanical behavior—from elastic deformation to fracture—is critical for advancing lightweight, high-strength designs. Contributions should explore experimental, computational, and theoretical approaches for characterizing properties such as stiffness, toughness, fatigue resistance, and damage tolerance under static, dynamic, and environmental loads.

Topics of interest:

Advanced testing methods (e.g., in-situ microscopy, digital image correlation);
Multiscale modeling (micromechanics, finite element analysis, peridynamics);
Novel composites (nanoparticle-reinforced, bio-based, or hybrid systems);
Failure analysis (fracture, delamination, creep, and aging effects);
Manufacturing-process-property relationships (3D printing, curing kinetics);
Image processing techniques for microstructural analysis and failure detection in polymer composites;
Machine learning models for strength, stiffness, and fatigue prediction in polymer composites;
Predictive analytics and digital twins for mechanical performance of polymer composites.

We welcome original research and reviews that bridge the gaps between material science and engineering applications, fostering innovation in the field of sustainable and high-performance polymer composites.

Dr. Ali Ercetin
Prof. Dr. Kubilay Aslantaş
Guest Editors

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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. 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.

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24 pages, 4064 KB

Open AccessArticle

Hardness and Surface Roughness of 3D-Printed ASA Components Subjected to Acetone Vapor Treatment and Different Production Variables: A Multi-Estimation Work via Machine Learning and Deep Learning

by Çağın Bolat, Furkancan Demircan, İlker Gür, Bekir Yalçın, Ramazan Şener and Ali Ercetin

Polymers 2025, 17(21), 2881; https://doi.org/10.3390/polym17212881 - 29 Oct 2025

Viewed by 396

Abstract

This paper analyzes the combined effects of acetone vapor treatment and 3D printing process parameters (layer thickness and infill rate) on the hardness and surface roughness of acrylonitrile styrene acrylate (ASA) components by using different machine learning and deep learning strategies for the first time in the technical literature. Considering the high-performance materials and aesthetic requirements of manufacturers, post-processing operations are highly critical for 3D-printed samples. ASA is a promising alternative, especially for the structural parts utilized in outdoor conditions like car outer components, electronic part housing, extreme sports equipment, and construction materials. However, it has to sustain hardness features against outer scratching, peeling, and indentations without losing its gloss. Together with the rising competitiveness in the search for a high-performance design with a perfect outer view, the combination of additive manufacturing and machine learning methods was implemented to enhance the hardness and surface quality properties for the first time in the literature. Concordantly, in this study, four different vaporizing durations (15, 45, 90, and 120 min.), three different layer thicknesses (0.1, 0.2, and 0.4 mm), and three different infill rates (25, 50, and 100%) were determined. According to both experimental and multi-way learning approaches, the results show that the support vector regressor (SVR) combined with one-dimensional convolutional neural networks (1D-CNNs) was the best approach for predictions. Gradient boosting (GB) and recurrent neural networks (RNNs) may also be preferable for low-error forecasting. Moreover, although there was a positive relationship between the layer thickness/infill rate and Shore D hardness outcomes, the highest levels were obtained at 45 min of vaporizing. Full article

(This article belongs to the Special Issue Polymer Composites: Mechanical Characterization)

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