# Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM
## 1 Introduction  
Carbon fiber is a new engineering material, which has become popular in aerospace, missile and rocket development [1; 2; 3; 4; 5]. In recent years, these materials have been developed rapidly in the field of civil construction, including in architecture and sports [6; 7]. The resin composite sheet of carbon fiber fabrics is an enhanced product with unidirectional (UD) carbon fiber, which is frequently used because of its excellent mechanical properties and easy repairaability [8]. Due to these superior properties, it is currently becoming a promising material in construction industries.  
However, because the appearances of different carbon fiber fabrics have no significant difference, different types of carbon fabric fibers are very easy to confuse during productions [9; 10], causing a large number of issues related to applications and construction. Also, there is a huge potential risk of compromised security in engineering processes using the resin composite sheet of carbon fiber fabrics without an exact classification [9]. So far, a direct classification approach for different fabrics is to measure the density [10]. However, this measurement is complicated and has high requirements with relevant instruments. Thus, there is still no study that reports an effective solution.  
To classify different types of resin composite sheets of carbon fiber fabrics in a simpler way, we should firstly note that differences in density may lead to differences in tensile strength and relevant properties, which can be easily measured by fabric strength machines. Therefore, we can rationally assume that measuring the fabric of carbon fiber using a fabric strength machine can help us obtain a method for classification. Then the question becomes simpler: how do we find out the relationship between the properties acquired from the fabric strength machine and the classification result? Theoretical studies have offered several equations that describe relevanttesting processes. Nevertheless, there is still a lack of an available method that can quantify different carbon fiber fabrics. Here, we successfully classify four different carbon fiber fabrics using a simple, defined quantification method with the strong classification capacity of machine learning techniques. Knowledge-based machine learning models were developed after the training process based on a large number of experimental data groups. To acquire enough experimental results, tensile stress and strain performances of four different types of carbon fiber fabrics were tested. All experimental data were measured from 231 samples in four different sample sizes. Based on the experimental data, novel machine learning techniques including artificial neural networks (ANNs) and support vector machine (SVM) were developed, respectively, for the classification of the four types of carbon fiber fabrics. This study, as an application research, aims at using user-friendly modeling techniques to help people classify different fabrics quickly based on the experimental data of tensile tests in research and practical applications. Therefore, the requirements of tensile strength can influence the selection of carbon fabrics by using our modeling techniques in practical applications.