P Radha, N Selvakumar (2016) Neural Networks for Predicting the Wear Properties of Sintered Ti-6Al-4V Composite Reinforced with Nano B 4 C Particle and Classiication using Data Mining Tools. Int J Comput Neural Eng. 3(3), 40-48.. 40 OPEN ACCESS http://scidoc.org/IJCNE.php International Journal of Computational & Neural Engineering (IJCNE) ISSN 2572-7389 Neural Networks for Predicting the Wear Properties of Sintered Ti-6Al-4V Composite Reinforced with Nano B 4 C Particle and Classiication using Data Mining Tools Research Article P Radha 1* , N Selvakumar 2 1 Associate Professor, Department of Computer Applications, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India. 2 Senior Professor, Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India. Introduction Titanium matrix composites (TMC) are good-looking materials for structural application in aircraft, automobile and military industries. TMC has high strength to weight ratio and good wear resistance. These have stimulated further research to grow their applications, by additional enhancement in the properties. Ti-6Al- 4V (Grade 5) is one of the supreme extensively used titanium alloys, because of its high speciic strength and good corrosion resistance. However, the foremost disadvantages of titanium and its alloys are low hardness and poor resistance to sliding wear [1-3]. Ceramic particles such as W, TiC, Al 2 O 3 and Boron carbide (B 4 C) are mostly used for reinforcement of TMC. B 4 C is one of the supreme promising ceramic materials due to its good properties, such as high strength, low density, extreme high hardness and good chemical stability [4-6]. Many techniques have been used for the fabrication of Ti-6Al-4V-B 4 C composites such as liquid phase methods and solid-state consolidation [7-10]. However, due to its poor wetting between Ti-Al-V and B 4 C, liquid phase method is dificult. Powder metallurgy (P/M) processing is an alternative method to fabricate of Ti-6Al-4V-B 4 C composites. Moreover in this process, the problem of non-wettability of B 4 C with molten TMC does not arise. Mechanical alloying is a good-looking P/M technique that produces uniform dispersion of the secondary particles in the matrix. By considering the above advantages, the P/M Lab uses composites preform (Ti-6Al-4V) with an addition of (2-10) wt. % of nano B 4 C using mechanical alloying method to improve the strength of materials. This strategy mixes the metal powders such as Ti, Al and V with B 4 C to avoid the dimension loss of composites due to friction and heat. While measuring the dry and high temperature wear using pin-on-disk testing machine, the temperature, load, sliding distance are varied for identifying the wear resistance of the prepared composites. Soft Computing models have been studied in recent years, with an objective of achieving human like performance in many ields Abstract This proposed work is to improve the strength and wear resistance of materials by reinforcing the composite preform (Ti- 6Al-4V) with an addition of (2-10) wt. % of nano boron carbide particles. The characterization was performed through Scanning electron microscope of above composites. While measuring wear using pin-on-disk testing machine, the tempera- ture, load, sliding distance are varied for identifying the nature of dry and high temperature wear of prepared composite. The output of this wear experimental work is fed to the soft computing based tool like Artiicial Neural Network for pre- dicting the wear properties such as speciic wear rate and coeficient of friction. Further, with respect to the temperature and B 4 C%, the wear properties are analysed using data mining tool like Decision Tree. Moreover, ixing the range of metal powders for classifying the wear properties of composite preforms can be automated by using Fuzzy logic. Keywords: Dry and High Temperature Wear; Ti-6Al-4V; Boron Carbide; Neural Networks; Fuzzy Approach; Data Mining. *Corresponding Author: P. Radha, Associate Professor, Department of Computer Applications, Mepco Schlenk Engineering College, Sivakasi - 626 005, Tamilnadu, India. Tel: 04562 235453 Fax: 04562 235111 E-mail: pradha@mepcoeng.ac.in Received: October 17, 2016 Accepted: December 12, 2016 Published: December 14, 2016 Citation: P Radha, N Selvakumar (2016) Neural Networks for Predicting the Wear Properties of Sintered Ti-6Al-4V Composite Reinforced with Nano B 4 C Particle and Classiication using Data Mining Tools. Int J Comput Neural Eng. 3(3), 40-48. doi: http://dx.doi.org/10.19070/2572-7389-160006 Copyright: P Radha © 2016. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.