This paper discusses the influence of fiber reinforcement on the properties of geopolymer concrete composites, based on fly ash, ground granulated blast furnace slag and metakaolin. Traditional concrete composites are brittle in nature due to low tensile strength. The inclusion of fibrous material alters brittle behavior of concrete along with a significant improvement in mechanical properties i.e., toughness, strain and flexural strength. Ordinary Portland cement (OPC) is mainly used as a binding agent in concrete composites. However, current environmental awareness promotes the use of alternative binders i.e., geopolymers, to replace OPC because in OPC production, significant quantity of CO2 is released that creates environmental pollution. Geopolymer concrete composites have been characterized using a wide range of analytical tools including scanning electron microscopy (SEM) and elemental detection X-ray spectroscopy (EDX). Insight into the physicochemical behavior of geopolymers, their constituents and reinforcement with natural polymeric fibers for the making of concrete composites has been gained. Focus has been given to the use of sisal, jute, basalt and glass fibers.

Polyvinylidene Fluoride (PVDF) piezoelectric electrospun nanofibers have been intensively used for sensing and actuation applications in the last decade. However, in most cases, random PVDF piezoelectric nanofiber mats have moderate piezoelectric response compared to aligned PVDF nanofibers. In this work, we demonstrate the effect of alignment conducted by a collector setup composed of two-metal bars with gab inside where the aligned fiber can be formed. That is what we called static aligned nanofibers, which is distinct from the dynamic traditional technique using a high speed rotating drum. The two-bar system shows a superior alignment degree for the PVDF nanofibers. Also, the effect of added carbon nanotubes (CNTs) of different concentrations to PVDF nanofibers is studied to observe the enhancement of piezoelectric response of PVDF nanofibers. Improvement of β-phase content of aligned (PVDF) nanofibers, as compared to randomly orientated fibers, is achieved. Significant change in the piezoelectricity of PVDF fiber is produced with added CNTs with saturation response in the case of 0.3 wt % doping of CNTs, and piezoelectric sensitivity of 73.8 mV/g with applied masses down to 100 g.

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In the fabric industry, textile yarns are the fundamental building blocks. Hence, visualizing and studying yarn structure is essential to understand the structure and behavior of the fibers. Obtaining the yarn’s cross-section images is crucial in the calculations of yarn’s porosity; furthermore, a more precise expansion for the fiber’s migration can be concluded from the cross-sectional images. In this paper, three different methods (microtome, micro-computed tomography, and epoxy grinding–polishing methods) to image and visualize the yarn’s cross-section are presented. The experimental techniques are compared in terms of result useability, time of preparation, and overall outcome of the cross-sectional image. The images can be used for fiber distribution, air gap calculation, and twist analysis as well. The fiber diameter distribution of polyester yarn was measured based on the images obtained by the three different methods; the average fiber diameter measured based on the combined data from the three different methods was found to be 10.90 ± 0.30 µm.

The present study deals with modal work that is a type of framework for structural dynamic testing of linear structures. Modal analysis is a powerful tool that works on the modal parameters to ensure the safety of materials and eliminate the failure possibilities. The concept of classification through this study is validated for isotropic and orthotropic materials, reaching up to a 100% accuracy when deploying the machine learning approach between the mode number and the associated frequency of the interrelated variables that were extracted from modal analysis performed by ANSYS. This study shows a new classification method dependent only on the knowledge of resonance frequency of a specific material and opens new directions for future developments to create a single device that can identify and classify different engineering materials.

Textile yarns are the fundamental building blocks in the fabric industry. The measurement of the diameter of the yarn textile and fibers is crucial in textile engineering as the diameter size and distribution can affect the yarn’s properties, and image processing can provide automatic techniques for faster and more accurate determination of the diameters. In this paper, facile and new methods to measure the yarn’s diameter and its individual fibers diameter based on image processing algorithms that can be applied to microscopic digital images. Image preprocessing such as binarization and morphological operations on the yarn image were used to measure the diameter automatically and accurately compared to the manual measuring using ImageJ software. Additionally, the image preprocessing, the circular Hough transform was used to count the individual fibers in a yarn’s cross-section and count the number of fibers. The algorithms were built and deployed in a MATLAB (R2020b, The MathWorks, Inc., Natick, Massachusetts, United States) environment. The proposed methods showed a reliable, fast, and accurate measurement compared to other different image measuring softwares, such as ImageJ.

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