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Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation
Author(s) -
Pati Pravat Ranjan,
Satapathy Alok
Publication year - 2015
Publication title -
polymers for advanced technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 90
eISSN - 1099-1581
pISSN - 1042-7147
DOI - 10.1002/pat.3421
Subject(s) - materials science , epoxy , composite material , lime , artificial neural network , composite number , refining (metallurgy) , glass fiber , slag (welding) , taguchi methods , metallurgy , computer science , machine learning
This article reports on the implementation of a soft computing technique based on artificial neural networks (ANNs) in analyzing the wear performance of a new class of hybrid composites filled with Linz–Donawitz slag (LDS). LDS is a major solid waste generated in huge quantities during steel making. It comes from slag formers such as burned lime/dolomite and from oxidizing of silica, iron etc. while refining the iron into steel in the LD furnace. In this work, hybrid composites consisting of short glass fiber (SGF) reinforced epoxy filled with different LDS content (0, 7.5, 15 and 22.5 wt%) are prepared by simple hand lay‐up technique. Solid particle erosion trials, as per ASTM G 76 test standards, are conducted on the composite samples following a well‐planned experimental schedule based on Taguchi design of experiments. Significant process parameters predominantly influencing the rate of erosion are identified. The study reveals that the LDS content is the most significant among various factors influencing the wear rate of these composites. Further, a model based on ANN for the prediction of erosion performance of these composites is implemented. The ANN prediction profiles for the characteristic wear properties exhibit very good agreement with the measured results demonstrating that a well‐trained network has been created. The simulated results explaining the effect of significant process variables on the wear rate indicate that the trained neural network possesses enough generalization capability of predicting wear rate even beyond the experimental range. Copyright © 2014 John Wiley & Sons, Ltd.