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
Figures :46
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
Scopus.
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
Aluminium MMCs.
[1] Agbeleye, A. A., Esezobor, D. E., Agunsoye, J. O., Balogun,
S. A., and Sosimi, A. A, “Prediction of the abrasive wear
behavior of heat-treated aluminium-clay composites using an
artificial neural network”, Journal of Taibah University for
Science, 2018.
[2] Ameen, H. A., Hassan, K. S., and Mubarak, E. M. M., “Effect
of loads, sliding speeds and times on the wear rate for
different materials”, American Journal of Scientific and
Industrial Research, 2(1), 2011, pp. 99-106.
[3] Attar, S., Nagaral, M., Reddappa, H.N., and Auradi, V.,
“Effect of B4C Particulates Addition on Wear Properties of
Al7025 Alloy Composites”, American Journal of Materials
Science, 5(3C), 2015, pp. 53-57.
[4] Basavarajappa, S. and Chandramohan, G., “Dry Sliding Wear
Behaviour of Hybrid Metal Matrix Composites”, Materials
Science, Vol. 11, No. 3, 2005.
[5] Bastwros, M. M. H., Esawi, A. M. K., and Wifi, A., “Friction
and wear behavior of Al-CNT composites”, Wear, Elsevier,
Vol. 307, Issue 1-2, 2013, pp. 164-173.
[6] Canakci, A., Varol, T., Ozsahin, S., and Ozkaya, S., “Artificial
Neural Network Approach to Predict the Abrasive Wear of
AA2024-B4C Composites”, Universal Journal of Materials
Science, 2(6), 2014, pp. 111-118.
[7] Derbal, I., Bourahla, N., Mebarki, A., and Bahar, R., “Neural
Network based prediction of ground time history responses”,
European Journal of Environmental and Civil Engineering,
2017.
[8] Ekka, K. K., Chauhan, S. R., and Varun, “Effect of
different reinforcements on sliding wear of aluminium matrix
composites using Taguchi design of experimental technique”,
Indian journal of Engineering and Materials Science, Vol. 22,
2015, pp. 195-202.
[9] Elango, G., and Raghunath, B.K., “Tribological Behavior of
Hybrid (LM25AL+SiC+TiO2) Metal Matrix Composites”,
International Conference on DESIGN AND
MANUFACTURING, 2013, pp. 671-680.
[10] Harti, J. I., Sridhar, B. R.,Vitala, H. R., and Jadhav, P. R.,
“Wear Behaviour of Al2219-TiC Particulate Metal Matrix”,
Composites, American Journal of Materials Science, 5(3C),
2015, pp. 34-37.
[11] Kumar, G. B. V., Pramod, R., Gouda, P. S. S., and Rao, C.
S. P., “Artificial Neural Networks for the Prediction of Wear
Properties of Al6061-TiO2 Composites”, IOP Conference
Series: Materials Science and Engineering, 225, 2017.
[12] Maheswaran, P., and Renald, C. J. T., “Investigation on
Wear Behaviour of Al6061-Al2O3-Graphite Hybrid Metal
Matrix Composites using Artificial Neural Network”,
International Journal of Current Engineering and
Technology, Special Issue-2, 2014.
[13] Mahmoud, T. S., “Artificial Neural Network Prediction of
the wear rate of powder metallurgy Al/Al2O3 metal matrix
composites”, Proceedings of the Institution of Mechanical
Engineers, Part L: Journal of Materials: Design and
Applications, Vol. 226, Issue 1, 2011, pp. 3-15.
[14] Miladinovic, S., Rankovic, V., Babic, M., Stojanovic, B., and
Velickovic, S., “Prediction of Tribological Behaviour of
Aluminium Matrix Hybrid Composites using Artificial
Neural Networks”, Serbian Tribology Society,
SERBIATRIB’17, 15th International Conference on
Tribology, 2017.
[15] Moorthy, A. A., Kumar, M. A., Satheesh, K. S., Natarajan,
N., and Palani, P. K., “Prediction of Tribological Properties
of AA2218 based Metal Matrix Composites by Artificial
Neural Network”, International Journal of Applied
Engineering Research, Vol. 10, No. 62, 2015.
[16] Patnaik, S.C., Swain, P.K., Mallik, P.K., and Sahoo, S. K.,
“Wear Characteristics of Aluminium-Graphite Composites
Produced by Stir Casting Technique”, Journal of Materials
and Metallurgical Engineering, Vol. 4, Issue 3, 2014.
[17] Pinto, J.W., Sujaykumar, G., and Sushiledra, R.M., “Effect
of Heat Treatment on Mechanical and Wear Characterization
of Coconut Shell Ash and E-glass Fibre Reinforced
Aluminium Hybrid Composites”, American Journal of
Materials Science, 6(4A), 2016, pp. 15-19.
[18] Rashed, F.S. and Mahmoud, T., “Prediction of wear
behaviour of A356/SiCP MMCs using neural networks”,
Tribology International, 42(5), 2009, pp. 642-648.
[19] Satyanarayana, G., Naidu, G. S., and Babu, N.H., “Artificial
Neural network and regression modelling to study the effect
of reinforcement and deformation on volumetric wear of red
mud nano particle reinforced aluminium matrix composites
synthesized by stir casting”, 2017.
[20] Thandalam, S.K., Ramanathan, S., and Sundarrajjan, S.,
“Synthesis, micro structural and mechanical properties of ex
situ Zircon particles (ZrSiO4) reinforced Metal Matrix Composites (MMCs): a Review”, Journal of Materials
Research and Technology, 4(3), 2015, pp. 333-347.
[21] Zhang, Y.Y., Zhang, Y.Z., Du, S.M., Song, C.F., Yang,
Z.H., and Shangguan, B., “Tribological properties of pure
carbon strip affected by dynamic contact force during
current-carrying sliding”, Tribology International,
Elsevier, Vol. 123, 2018, pp. 256-265.
