Investigation and modeling on protective textiles using artificial neural networks for defense applications

Downloads

Downloads per month over past year

Ramaiah, G.B. and Radhalakshmi, Y.C. and Gurumurthy, K.S. (2010) Investigation and modeling on protective textiles using artificial neural networks for defense applications. Materials Science and Engineering B: Solid-State Materials for Advanced Technology, 168 (1). pp. 100-105. ISSN 0921-5107

[img]
Preview
Text
1-s2.0-S0921510709005546-main.pdf - Published Version

Download (437kB) | Preview
Official URL: https://doi.org/10.1016/j.mseb.2009.12.029

Abstract

Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25–40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

Item Type: Article
Uncontrolled Keywords: Specific modulus; Specific tenacity; Kevlar 29; Fragment simulation projectile; Back-propagation neural networks; Dissipated energy; Bayesian information criterion
Subjects: Faculty of Engineering > Computer Science & Information Science Engineering
Divisions: University Visvesvarayya College of Engineering > Department of Computer Science and Information Science Engineering
Depositing User: Mr . Chandrashekar
Date Deposited: 22 Oct 2016 08:56
Last Modified: 22 Oct 2016 08:56
URI: http://eprints-bangaloreuniversity.in/id/eprint/6818

Actions (login required)

View Item View Item