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Computational Intelligence for Analysing the Mechanical Properties of AA 2219 - (B4C + h-BN) Hybrid Nano Composites Processed by Ultrasound Assisted Casting

Radha P., Selvakumar N., Harichandran R.

Archives of Metallurgy and Materials

2019

Vol. 64, iss. 3

1163--1173

The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B4 C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B4 C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B4 C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro/nano composites with the inclusion of B4 C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model.