Classification of Textile Polymer Composites: Recent Trends and Challenges

Abstract

Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

“…Recently, ANN has shown its effectiveness in the prediction of not only tensile strength but many other parameters including dye removal efficiency and functional properties of composites [ 25 , 26 , 27 , 28 ]. ANN has the advantages of high nonlinearity resolution, self-learning and mapping capability between input and output variables without introducing a mathematical model between nonlinear data.…”

Aerogels for Biomedical, Energy and Sensing Applications

“…Scientifically, aerogels are highly porous, light-weight and unique solid-state structures composed of three dimensional (3D) interconnected networks filled with a huge number of air pores [ 2 ]. These air-filled pores enhance the physicochemical properties and the structural characteristics in macroscale as well as integrate typical characteristics of aerogels, e.g., low density, high porosity and some specific properties of their constituents [ 3 , 4 ]. These extraordinary and attractive characteristics endow aerogels as a first choice in highly sensitive sensing and energy applications, e.g., biosensors [ 5 , 6 ], gas sensors [ 7 ], pressure strain sensors [ 8 ], supercapacitors [ 9 ], catalysts [ 10 , 11 ], energy storage [ 12 , 13 ], piezoelectric [ 14 ], thermal insulators [ 15 , 16 ] and ion batteries [ 17 ].…”

Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

“…On the other hand, DNN models have achieved human-level performance and have shown great success in different real-world applications, including computer vision [ 16 ], textile process, biomedical engineering [ 17 ], material engineering [ 18 ]. DNN is an efficient machine learning tool suitable for the prediction of output parameters from input variables where there is an unknown relationship exists between input and output variables [ 19 , 20 , 21 ]. In recent years, DNN has been widely used to predict various properties of textiles.…”