Prediction of Fracture Toughness of Pultruded Composites Based on Supervised Machine Learning

Prediction of mechanical properties is an essential part of material design. State-of-the-art simulation-based prediction requires data on microstructure and inter-component interactions of material. However, due to high costs and time limitations, such parameters, which are especially required for the simulation of advanced properties, are not always available. This paper proposes a data-driven approach to predicting the labor-consuming fracture toughness based on a series of standard, easy-to-measure mechanical characteristics. Three supervised machine-learning (ML) models (artificial neural networks, a random forest algorithm, and gradient boosting) were designed and tested for the prediction of mechanical properties of pultruded composites. A considerable dataset of mechanical properties was acquired as results of standard tensile, compression, flexure, in-plane shear, and Charpy tests and utilized as the input to predict the fracture toughness. Furthermore, this study investigated the correlations between the obtained mechanical characteristics. Analysis of ML performance showed that fracture toughness had the highest correlations with longitudinal bending and transverse tension and a strong correlation with the longitudinal compression modulus and tensile strength. The gradient boosting decision tree-based algorithms demonstrated the best prediction performance for fracture toughness, with an MSE less than 10% of the average value, providing a prediction within the range of experimental error. The ML algorithms showed potential in terms of determining which macro-level parameters can be used to predict micro-level material characteristics and how. The results provide inspiration for future pultruded composite material design and can enhance the numerical simulations of material

Super-Resolution Processing of Synchrotron CT Images for Automated Fibre Break Analysis of Unidirectional Composites

Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is not sufficient for a fully automated analysis of continuous mechanical deformations. We therefore investigate the possibility of increasing the quality of low-resolution in situ scans by means of super-resolution (SR) using 3D deep learning techniques, thus facilitating the subsequent fibre break identification. We trained generative neural networks (GAN) on datasets of high—(0.3 μm) and low-resolution (1.6 μm) statically acquired images. These networks were then applied to a low-resolution (1.1 μm) noisy image of a continuously loaded specimen. The statistical parameters of the fibre breaks used for the comparison are the number of individual breaks and the number of 2-plets and 3-plets per specimen volume. The fully automated process achieves an average accuracy of 82% of manually identified fibre breaks, while the semi-automated one reaches 92%. The developed approach allows the use of faster, low-resolution in situ tomography without losing the quality of the identified physical parameters

Micro-CT based structure tensor analysis of fibre orientation in random fibre composites versus high-fidelity fibre identification methods

The fibre orientation distribution controls the mechanical properties of random fibre composites. Generally accepted methods for its characterisation involve identification of fibres or their ellipsoidal cross sections as individual objects, requiring high image resolution and high computational resources. This paper investigates whether structure tensor analysis can be an alternative and whether it can work with lower resolution images. Micro-computed X-ray tomography images of random glass fibre/polypropylene injection moulded composites were processed using ellipsometry on 2D slices, 3D fibre identification (Avizo software) and analysis of the structure tensor (VoxTex software). The images had resolutions of 1.4, 3.2, 8 and 16 mu m per pixel, compared to an average glass fibre diameter of 17 mu m All the methods yielded similar results for high-resolution images (1.4 and 3.2 mu m). The high-fidelity, direct identification of fibres failed for low-resolution images, but the structure tensor analysis still yielded results close to the high-resolution scans

Multifunctional Elastic Nanocomposites with Extremely Low Concentrations of Single-Walled Carbon Nanotubes

Funding Information: This work was supported by Russian Foundation for Basic Research Grant No. 18-29-06071. We thank the Council on grants of the President of the Russian Federation grant number HIII-1330.2022.1.3. F.F. and D.K. thank Russian Science Foundation, Grant No. 21-73-10288 for support of impedance spectroscopy studies. Publisher Copyright: © 2022 American Chemical Society. All rights reserved.Stretchable and flexible electronics has attracted broad attention over the last years. Nanocomposites based on elastomers and carbon nanotubes are a promising material for soft electronic applications. Despite the fact that single-walled carbon nanotube (SWCNT) based nanocomposites often demonstrate superior properties, the vast majority of the studies were devoted to those based on multiwalled carbon nanotubes (MWCNTs) mainly because of their higher availability and easier processing procedures. Moreover, high weight concentrations of MWCNTs are often required for high performance of the nanocomposites in electronic applications. Inspired by the recent drop in the SWCNT price, we have focused on fabrication of elastic nanocomposites with very low concentrations of SWCNTs to reduce the cost of nanocomposites further. In this work, we use a fast method of coagulation (antisolvent) precipitation to fabricate elastic composites based on thermoplastic polyurethane (TPU) and SWCNTs with a homogeneous distributionof SWCNTs in bulk TPU. Applicability of the approach is confirmed by extra low percolation threshold of 0.006 wt % and, as a consequence, by the state-of-the-art performance of fabricated elastic nanocomposites at very low SWCNT concentrations for strain sensing (gauge factor of 82 at 0.05 wt %) and EMI shielding (efficiency of 30 dB mm-1at 0.01 wt %).Peer reviewe

Development of an Environmentally Friendly Technology for the Treatment of Aqueous Solutions with High-Purity Plasma for the Cultivation of Cotton, Wheat and Strawberries

The microwave setup for obtaining plasma-activated water (PAW) has been created. PAW contains significant concentrations of H2O2 and NO3−, has a reduced content of O2, high conductivity, a high redox potential and low pH. Likewise, the specific electrical conductivity and concentration of H2O2 and NO3− linearly depend on the treatment time. These parameters are simple and convenient markers for controlling the preparation of PAW. It has been established that PAW solutions with a concentration of 0.5–1.0% increase the germination energy, protect against fusarium and hyperthermia in cotton, wheat and strawberry seeds. In addition, PAWs have a positive effect on the growth rate of plants in the early stages of development. The use of PAW provides significant benefits over the chemical preparations Dalbron and Bakhor, so-called seed germination stimulators (SDS)