3D Woven Composites: From Weaving to Manufacturing

Manufacturing near-net shape preforms of fibre-reinforced composites has received growing interest from industry. Traditionally, a preform was made from 2D fabrics, but recently, it has been shown that 3D textiles can be used with success; with weaving being the predominant technology for carbon fibre composites. In 3D weaving, weft, warp and binder fibres run across, along and through the fabrics in the X, Y and Z directions, respectively. Producing a unitised single-piece fabric and subsequently reducing the takt time required for rapid composite manufacturing are two of the main advantages of using 3D woven preforms. Weaving of 3D fabrics, manufacturing of 3D composites, physical characterisation and mechanical testing of infused composites samples are discussed in this chapter. Finally, a large automotive composite made of single-piece 3D woven preform was manufactured and presented for demonstration

Energy Efficient Encryption Algorithm for Low Resources Devices

Saving energy is one of the most challenging aspects in the wireless network devices. Such devices are connected together to perform a certain task. A well-known example of these structures is the Wireless Sensor Network (WSN). Distributed WSN consists of several spread nodes in a harsh area. Therefore, once network has been established sensors replacement is not a possible option before at least five years which called network lifetime. So, it is a necessity to develop specific energy aware algorithms that could save battery lifetime as much as possible. Security and Privacy are the vital elements which need to be addressed to hold up to the trust of users in WSN environment. Because the majority of modern cryptographic algorithms were designed for desktop/server environments, many of these algorithms cannot be implemented in the constrained devices used by these networks. Symmetric key algorithms are a typically efficient and fast cryptosystem, so it has significant applications in many realms. For a WSN with constraint computational resources, the cryptosystem based on symmetric key algorithms is extremely suitable for such an agile and dynamic environment. Therefore, a Simple Lightweight Encryption Algorithm (SLEA) based on addition and subtraction operations and compact Substitution-boxes (S-boxes) is proposed for wireless networks due to its low energy consumption, simple hardware requirements and suitable level of security. In addition, the algorithm tries to overcome the limitations of both public- and symmetric-key protocols. It relies on a smart version of Feistel structure

Intelligent Model for Data Analytical Study of Coronavirus COVID-19 Databases

The pandemic coronavirus COVID-19 spread around the world with deaths exceeding that of SARS. COVID-19 is believed to have been transmitted from animals, especially from bats, and the virus is transmitted from person to person over time. This paper will help countries to make decisions that encourage access to corrected values and get some indication as to whether there are other factors that affect the spread of COVID-19, via methods such as by increasing the daily test rate. This paper presents an intelligent model for analyzing data collected from the countries affected by the COVID-19 virus. It considers the total number of tests that each country has undergone, the number of international tourist arrivals in each country, the percentage of employment, the life expectancy at birth, the median age, the population density, the number of people aged 65 years or older in millions, and the sex ratio. The proposed model is based on machine learning approaches using k-Means as a clustering approach, Support Vector Machine (SVM) as a classifier, and wrapper as a feature extraction approach. It consists of three phases of pre-processing the data collected, the discovery of outlier cases, the selection of the most effective features for each of the total infected, deaths, critical and recovery cases, and the construction of prediction models. Experimental results show that the extracted features of the wrapper technique have shown that it is more capable of fitting and predicting data than the Correlation-Based Feature Selection, Correlation Attribute Evaluation, Information Gain, and Relief Attribute Evaluation techniques. The SVM classifier also achieved the highest accuracy compared to other classification algorithms for predicting total infected, fatal, critical, and recovery cases

Intelligent Model for Data Analytical Study of Coronavirus COVID-19 Databases

The pandemic coronavirus COVID-19 spread around the world with deaths exceeding that of SARS. COVID-19 is believed to have been transmitted from animals, especially from bats, and the virus is transmitted from person to person over time. This paper will help countries to make decisions that encourage access to corrected values and get some indication as to whether there are other factors that affect the spread of COVID-19, via methods such as by increasing the daily test rate. This paper presents an intelligent model for analyzing data collected from the countries affected by the COVID-19 virus. It considers the total number of tests that each country has undergone, the number of international tourist arrivals in each country, the percentage of employment, the life expectancy at birth, the median age, the population density, the number of people aged 65 years or older in millions, and the sex ratio. The proposed model is based on machine learning approaches using k-Means as a clustering approach, Support Vector Machine (SVM) as a classifier, and wrapper as a feature extraction approach. It consists of three phases of pre-processing the data collected, the discovery of outlier cases, the selection of the most effective features for each of the total infected, deaths, critical and recovery cases, and the construction of prediction models. Experimental results show that the extracted features of the wrapper technique have shown that it is more capable of fitting and predicting data than the Correlation-Based Feature Selection, Correlation Attribute Evaluation, Information Gain, and Relief Attribute Evaluation techniques. The SVM classifier also achieved the highest accuracy compared to other classification algorithms for predicting total infected, fatal, critical, and recovery cases

Effect of Unit-Cell Size on the Barely Visible Impact Damage in Woven Composites

The effect of the weaving architecture and the z-binding yarns, for 2D and 3D woven composites on the low-velocity impact resistance of carbon fibre reinforced composites, is investigated and benchmarked against noncrimp fabric (NCF). Four architectures, namely: NCF, 2D plain weave (2D-PW), 3D orthogonal: plain (ORT-PW) and twill (ORT-TW), were subjected to 15 J impact using a 16 mm-diameter, 6.7 kg hemispherical impactor. Nondestructive techniques, including ultrasonic C-scanning, Digital Image Correlation (DIC) and X-ray computed tomography (CT) were used to map and quantify the size of the induced barely visible impact damage (BVID). The energy absorption of each architecture was correlated to the damage size: both in-plane and in-depth directions. The 3D architectures, regardless of their unit-cell size, demonstrated the highest impact resistance as opposed to 2D-PW and the NCF. X-ray CT segmentation showed the effect of the higher frequency of the z-binding yarns, in the ORT-PW case, in delamination and crack arresting even when compared to the other 3D architecture (ORT-TW). Among all the architectures, ORT-PW exhibited the highest damage resistance with the least damage size. This suggests that accurate design of the z-binding yarns’ path and more importantly its frequency in 3D woven architectures is essential for impact-resistant composite structures