This paper aims at predicting the wear behavior of Al2219 alloy reinforced with TiC micro particles (in different weight
fractions) in an unconventional way that leads to new latitude of soft computing. The dynamism of this work lies in the fact that it puts
stress on mapping between two variegated domains of engineering i.e. Artificial Neural Network (ANN) is exercised on the province of
Tribology. Wear is the problem of components that requires the replacement of the segments of assemblies frequently, thus making it
necessary to minimize the wear rate. Feed Forward Back Propagation Network (FFBN) has been proven at its best in prediction using
TANSIG and LOGSIG transfer functions due to the back propagation of the output errors, providing incomparable and significant
accuracy. Hence this analysis of prediction emanating a new scope in the field of aerospace, aircraft, defense and automotive
applications, is also an innovation in the discipline of Tribology.
Published In:IJCSN Journal Volume 7, Issue 2
Date of Publication : April 2018
Pages : 99-114
Tables : 27
Anindita Das Bhattacharjee : started her career in industry as a
trainee software developer for a year. She has done M. Tech in
Computer Science from National Institute of Technology (NIT),
Durgapur. She secured a position of First Class Second in M.
Tech. Currently she is working in Swami Vivekananda Institute of
Science and Technology. She has been teaching for about 10
years in Computer Science. She is an author of a book “Artificial
Intelligence and Soft Computing for Beginners” published in 2013
and author of book chapters in the book “Intelligent Analysis for
Multimedia Information” published by IGI Global and indexed in
Disha Chanda : is a final year B. Tech student of Computer Science
& Engineering department in Swami Vivekananda Institute of
Science & Technology (SVIST) under Maulana Abul Kalam Azad
University of Technology (MAKAUT).
Artificial Neural Network, Feed Forward Back Propagation, Hidden Layer, Regression, Transfer Function, Tribology
The investigation and outcome interprets the triumph of
application of Machine Learning and Soft Computing in
the terrain of Tribology in predicting the wear behaviour of
TiC reinforced Al2219 alloy. This research focuses mainly
on the behavioural changes of FFBN along with its
different transfer functions. The most promising analytical
result was achieved when it was found that there is no such
specific transfer function that can behave well on a specific
dataset. It was achieved that different transfer functions
performs the best on different datasets (Table: 26, 27).
Finally it can be concluded that the sole behaviour of the
ANN is highly constrained by the particular dataset on
which it is applied for prediction. It opens up the region of
applicability of ANN prediction in the field of different
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