期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2018
卷号：7
期号：2
页码：99-114
出版社：IJCSN publisher
摘要：This paper aims at predicting the wear behavior of Al2219 alloy reinforced with TiC micro particles (in different weightfractions) in an unconventional way that leads to new latitude of soft computing. The dynamism of this work lies in the fact that it putsstress on mapping between two variegated domains of engineering i.e. Artificial Neural Network (ANN) is exercised on the province ofTribology. Wear is the problem of components that requires the replacement of the segments of assemblies frequently, thus making itnecessary to minimize the wear rate. Feed Forward Back Propagation Network (FFBN) has been proven at its best in prediction usingTANSIG and LOGSIG transfer functions due to the back propagation of the output errors, providing incomparable and significantaccuracy. Hence this analysis of prediction emanating a new scope in the field of aerospace, aircraft, defense and automotiveapplications, is also an innovation in the discipline of Tribology.
关键词：Artificial Neural Network; Feed Forward Back Propagation; Hidden Layer; Regression; Transfer Function; Tribology
