Fiber recognition with machine learning methods by fiber tensile fracture via acoustic emission method

Abstract

Energy release usually accompanies the single-fiber tensile fracture, and can be monitored using acoustic emission technology. Generated during the process of molecular structure fracture of various fibers, the acoustic emission signals can be extracted to identify different fracture types of fiber, which is especially important to the yarn formation process. In this study, a low-noise fiber-stretching device was employed to process the weak-intensity signal generated during fiber tensile fracture; in addition, the Hilbert–Huang transform (HHT), principal component analysis (PCA) and least squares support vector machine (LSSVM) algorithms were combined to identify the collected acoustic emission signals of polyester and cotton fibers. At the same time, it was verified that compared with the single-fiber breaking acoustic emission signal obtained by the electronic single-fiber strength tester, the signal acquisition device based on pneumatic components proposed in this paper can significantly improve the signal-to-noise ratio of the signal. According to the algorithm recognition results, the recognition rate of the two fibers increased from 74% to 95%.The experimental results indicate successful measurements of different fractures of two types of fiber.

Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine

“…AR model is a time series model, and the autoregressive parameters of the model are sensitive to changes in status and can be used as feature vectors for acoustic emission signal identification [18] [19]. LSSVM is an improved SVM with stronger generalization ability, which has better advantages in small sample classification recognition [20] [21]. Based on the above research results, the paper uses the LMD algorithm to decompose the electromagnetic acoustic emission signal to obtain the PF component signal, and selects the optimal PF component signal according to the energy occupation ratio method.…”

Behavior of woven fabric composite under tensile loads in different directions using acoustic emission

“…11 Moreover, this method is used to recognize fibers. 12 Some examine the tensile of yarn by the acoustic emission method and the fiber breakage sequence in the yarn. 13 Some researchers have studied uni-directional laminates under tension by acoustic emission.…”

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

“…(3) its implementation is easy; (4) its network topology is no need to be determined in advance, which can be generated automatically when the training process terminates; (5) it has high generalized capability which can avoid local minimum [25]. Due to these prominent advantages, SVR has been demonstrated much success in the application in textile and fashion industry, such as prediction textile dying process parameters [26], yarns characteristics [27,28], fabric qualities [29], fabric contents [30,31] and human body measurements [32].…”