Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.4
3.2
Journals
Materials
Special Issues
Advances in Textile-Based Composites and Polymers: Machine Learning Predictions, Structure...
materials-logo
Submit to Materials Review for Materials Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Textile-Based Composites and Polymers: Machine Learning Predictions, Structure Optimization and Smart Applications

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Polymeric Materials".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 2614

Special Issue Editors

Dr. Pooria Khalili

E-Mail Website
Guest Editor
Swedish Centre for Resource Recovery, Faculty of Textiles, Engineering and Business, University of Borås, SE-50190 Borås, Sweden
Interests: composites; natural fiber composites; testing; processing; simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Nancy Abdallah

E-Mail Website
Guest Editor Assistant
Faculty of Textiles, Engineering and Business (including The Swedish School of Textiles)—Department of Engineering, University of Borås, Borås, Sweden
Interests: machine learning; modelling

Special Issue Information

Dear Colleagues,

The Special Issue presents a comprehensive overview of the latest advances in the intersection of textiles, composites, and polymers. Focusing on the integration of machine learning, the issue explores predictive modeling in order to understand complex material behaviors. Researchers delve into the application of machine learning algorithms for the prediction and optimization of the structural designs of textile-based composites.

Beyond theoretical discussions, the also Special Issue emphasizes practical implementations, demonstrating how smart textiles are deployed across various domains. From predicting material responses to optimizing structures and incorporating intelligent applications, this issue offers a holistic perspective on the developing landscape of textile-based composites and polymers. Specifically targeting researchers, engineers, and practitioners, this collection of articles serves as a valuable resource for the latest advancements in this dynamic and interdisciplinary field.

Dr. Pooria Khalili
Guest Editor

Dr. Nancy Abdallah
Guest Editor Assistant

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

  • composites and polymers
  • machine learning predictions
  • structure optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 6956 KiB  
Article
Multifunctional Sensor Array for User Interaction Based on Dielectric Elastomers with Sputtered Metal Electrodes
by Sebastian Gratz-Kelly, Mario Cerino, Daniel Philippi, Dirk Göttel, Sophie Nalbach, Jonas Hubertus, Günter Schultes, John Heppe and Paul Motzki
Materials 2024, 17(23), 5993; https://doi.org/10.3390/ma17235993 - 6 Dec 2024
Cited by 2 | Viewed by 1188
Abstract
The integration of textile-based sensing and actuation elements has become increasingly important across various fields, driven by the growing demand for smart textiles in healthcare, sports, and wearable electronics. This paper presents the development of a small, smart dielectric elastomer (DE)-based sensing array [...] Read more.
The integration of textile-based sensing and actuation elements has become increasingly important across various fields, driven by the growing demand for smart textiles in healthcare, sports, and wearable electronics. This paper presents the development of a small, smart dielectric elastomer (DE)-based sensing array designed for user control input in applications such as human–machine interaction, virtual object manipulation, and robotics. DE-based sensors are ideal for textile integration due to their flexibility, lightweight nature, and ability to seamlessly conform to surfaces without compromising comfort. By embedding these sensors into textiles, continuous user interaction can be achieved, providing a more intuitive and unobtrusive user experience. The design of this DE array draws inspiration from a flexible and wearable version of a touchpad, which can be incorporated into clothing or accessories. Integrated advanced machine learning algorithms enhance the sensing system by improving resolution and enabling pattern recognition, reaching a prediction performance of at least 80. Additionally, the array’s electrodes are fabricated using a novel sputtering technique for low resistance as well as high geometric flexibility and size reducibility. A new crimping method is also introduced to ensure a reliable connection between the sensing array and the custom electronics. The advantages of the presented design, data evaluation, and manufacturing process comprise a reduced structure size, the flexible adaptability of the system to the respective application, reliable pattern recognition, reduced sensor and line resistance, the adaptability of mechanical force sensitivity, and the integration of electronics. This research highlights the potential for innovative, highly integrated textile-based sensors in various practical applications. Full article
(This article belongs to the Special Issue Advances in Textile-Based Composites and Polymers: Machine Learning Predictions, Structure Optimization and Smart Applications)
►▼ Show Figures

Graphical abstract

11 pages, 3735 KiB  
Article
Research on Natural Fiber Microstructure Detection Method Based on CA-DeepLabv3+
by Shuaishuai Lv, Xiaoyuan Li, Hitoshi Takagi, Zhengjie Hou, Yifei Zhai, Linfei Chen and Hongjun Ni
Materials 2024, 17(23), 5942; https://doi.org/10.3390/ma17235942 - 4 Dec 2024
Viewed by 948
Abstract
Natural fibers exhibit noticeable variations in their cross-sections, and measurements assuming a circular cross-section can lead to errors in the values of their properties. Providing more accurate geometric information of fiber cross-sections is a key challenge. Based on microscopic images of natural fiber [...] Read more.
Natural fibers exhibit noticeable variations in their cross-sections, and measurements assuming a circular cross-section can lead to errors in the values of their properties. Providing more accurate geometric information of fiber cross-sections is a key challenge. Based on microscopic images of natural fiber structures, this paper proposes a natural fiber microstructure detection method based on the CA-DeepLabv3+ network model. The study investigates a natural fiber microstructure image segmentation algorithm that uses MobileNetV2 as the feature extraction backbone network, optimizes the Atrous Spatial Pyramid Pooling (ASPP) module through cascading, and embeds an Efficient Multi-scale Attention (EMA) mechanism. The results show that the algorithm proposed in this paper can accurately segment the microstructures of multiple types of natural fibers, achieving an average pixel accuracy (mPA) of 95.2% and a mean Intersection over Union (mIoU) of 90.7%. Full article
(This article belongs to the Special Issue Advances in Textile-Based Composites and Polymers: Machine Learning Predictions, Structure Optimization and Smart Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop