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Appl. Sci. 2016, 6(8), 209; doi:10.3390/app6080209

Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM

1
College of Light Industry, Textile and Food Engineering, Sichuan University, Chengdu 610065, Sichuan, China
2
Department of Art, Jincheng College of Sichuan University, Chengdu 610000, Sichuan, China
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Dawson
Received: 31 March 2016 / Revised: 14 July 2016 / Accepted: 15 July 2016 / Published: 27 July 2016
(This article belongs to the Special Issue Applied Artificial Neural Network)
View Full-Text   |   Download PDF [2349 KB, uploaded 27 July 2016]   |  

Abstract

Carbon fiber fabrics are important engineering materials. However, it is confusing to classify different carbon fiber fabrics, leading to risks in engineering processes. Here, a classification method for four types of carbon fiber fabrics is proposed using artificial neural networks (ANNs) and support vector machine (SVM) based on 229 experimental data groups. Sample width, breaking strength and breaking tenacity were set as independent variables. Quantified numbers for the four carbon fiber fabrics were set as dependent variables. Results show that a multilayer feed-forward neural network with 21 hidden nodes (MLFN-21) has the best performance for classification, with the lowest root mean square error (RMSE) in the testing set. View Full-Text
Keywords: carbon fiber fabrics; classification; machine learning; artificial neural networks; support vector machine carbon fiber fabrics; classification; machine learning; artificial neural networks; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, M.; Li, Z.; He, W. Classifying Four Carbon Fiber Fabrics via Machine Learning: A Comparative Study Using ANNs and SVM. Appl. Sci. 2016, 6, 209.

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