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J. Compos. Sci.
Special Issues
Machine Learning in Composites
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Machine Learning in Composites

A special issue of Journal of Composites Science (ISSN 2504-477X). This special issue belongs to the section "Composites Modelling and Characterization".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 14121

Special Issue Editor

Dr. Sunil Chandrakant Joshi

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 50 Nanyang Ave, Singapore 639798, Singapore
Interests: thermoplastic composites; fibre reinforced aerospace composites and structures; multi-functional composites; numerical simulation and optimization of composite manufacturing processes; analysis and testing of thermal controls for micro-satellites; thermo-mechanical analysis of coated and composite structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Composites have gained a vital place in various sectors due to their high specific properties and flexibility in the design of layups. However, ample testing is required to ensure their applicability in many engineering fields. The very nature of these materials and their manufacturing make their testing time-consuming, expensive, and prone to a large scatter. There is a need to look for new ways to assess their performance and reliability.

Nowadays, data analysis and prognostic techniques, such as machine learning, are being used across many industries to improve efficiency and achieve results that are otherwise impossible by traditional methods. However, to achieve accurate results through data analysis, a large amount of data is required to develop, train, and validate machine learning models. This is certainly challenging in the case of composites.

This Special Issue intends to gather and publish original research papers that address all such challenges in using machine and deep learning for composites. Papers are sought that provide successful solutions and techniques for data augmentation based on knowledge rather than experiments. Original works on the use of machine learning in composites design, optimization in manufacturing, and performance prediction are also welcome.

Dr. Sunil Chandrakant Joshi
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. Journal of Composites Science is an international peer-reviewed open access monthly 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

  • advanced composites
  • composites manufacturing
  • polymer composites
  • engineered multiscale composites
  • data boosting
  • machine learning
  • deep learning
  • neural network
  • prognostics

Published Papers (3 papers)

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Research

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20 pages, 19575 KiB  
Article
Characterisation of Composite Materials for Wind Turbines Using Frequency Modulated Continuous Wave Sensing
by Wenshuo Tang, Jamie Blanche, Daniel Mitchell, Samuel Harper and David Flynn
J. Compos. Sci. 2023, 7(2), 75; https://doi.org/10.3390/jcs7020075 - 10 Feb 2023
Cited by 3 | Viewed by 2141
Abstract
Wind turbine blades (WTBs) are critical sub-systems consisting of composite multi-layer material structures. WTB inspection is a complex and labour intensive process, and failure of it can lead to substantial energy and economic losses to asset owners. In this paper, we proposed a [...] Read more.
Wind turbine blades (WTBs) are critical sub-systems consisting of composite multi-layer material structures. WTB inspection is a complex and labour intensive process, and failure of it can lead to substantial energy and economic losses to asset owners. In this paper, we proposed a novel non-destructive evaluation method for blade composite materials, which employs Frequency Modulated Continuous Wave (FMCW) radar, robotics and machine learning (ML) analytics. We show that using FMCW raster scan data, our ML algorithms (SVM, BP, Decision Tree and Naïve Bayes) can distinguish different types of composite materials with accuracy of over 97.5%. The best performance is achieved by SVM algorithms, with 94.3% accuracy. Furthermore, the proposed method can also achieve solid results for detecting surface defect: interlaminar porosity with 80% accuracy overall. In particular, the SVM classifier shows highest accuracy of 92.5% to 98.9%. We also show the ability to detect air voids of 1mm differences within the composite material WT structure with 94.1% accuracy performance using SVM, and 84.5% using Naïve Bayes. Lastly, we create a digital twin of the physical composite sample to support the integration and qualitative analysis of the FMCW data with respect to composite sample characteristics. The proposed method explores a new sensing modality for non-contact surface and subsurface for composite materials, and offer insights for developing alternative, more cost-effective inspection and maintenance regimes. Full article
(This article belongs to the Special Issue Machine Learning in Composites)
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12 pages, 2342 KiB  
Article
Prediction of Composite Mechanical Properties: Integration of Deep Neural Network Methods and Finite Element Analysis
by Kimia Gholami, Faraz Ege and Ramin Barzegar
J. Compos. Sci. 2023, 7(2), 54; https://doi.org/10.3390/jcs7020054 - 3 Feb 2023
Cited by 5 | Viewed by 3087
Abstract
Extracting the mechanical properties of a composite hydrogel; e.g., bioglass (BG)–collagen (COL), is often difficult due to the complexity of the experimental procedure. BGs could be embedded in the COL and thereby improve the mechanical properties of COL for bone tissue engineering applications. [...] Read more.
Extracting the mechanical properties of a composite hydrogel; e.g., bioglass (BG)–collagen (COL), is often difficult due to the complexity of the experimental procedure. BGs could be embedded in the COL and thereby improve the mechanical properties of COL for bone tissue engineering applications. This paper proposed a deep-learning-based approach to extract the mechanical properties of a composite hydrogel directly from the microstructural images. Four datasets of various shapes of BGs (9000 2D images) generated by a finite element analysis showed that the deep neural network (DNN) model could efficiently predict the mechanical properties of the composite hydrogel, including the Young’s modulus and Poisson’s ratio. ResNet and AlexNet architecture were tuned to ensure the excellent performance and high accuracy of the proposed methods with R-values greater than 0.99 and a mean absolute error of the prediction of less than 7%. The results for the full dataset revealed that AlexNet had a better performance than ResNet in predicting the elastic material properties of BGs-COL with R-values of 0.99 and 0.97 compared to 0.97 and 0.96 for the Young’s modulus and Poisson’s ratio, respectively. This work provided bridging methods to combine a finite element analysis and a DNN for applications in diverse fields such as tissue engineering, materials science, and medical engineering. Full article
(This article belongs to the Special Issue Machine Learning in Composites)
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Review

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36 pages, 8611 KiB  
Review
Artificial Intelligence in Predicting Mechanical Properties of Composite Materials
by Fasikaw Kibrete, Tomasz Trzepieciński, Hailu Shimels Gebremedhen and Dereje Engida Woldemichael
J. Compos. Sci. 2023, 7(9), 364; https://doi.org/10.3390/jcs7090364 - 1 Sep 2023
Cited by 4 | Viewed by 8339
Abstract
The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This [...] Read more.
The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain. Full article
(This article belongs to the Special Issue Machine Learning in Composites)
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