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  A Machine Learning Advent in the Prediction Analysis of Wear Behavior of TiC Reinforced Al2219 Metal Matrix Composite  
  Authors : Anindita Das Bhattacharjee; Disha Chanda
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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.


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