doi: 10.7763/IJMO.2011.V1.15
A Grey-based Taguchi Method for Wear Assesment of Red Mud Filled Polyester Composites
Siba Sankar Mahapatra
,
Saurav Datta
- Department of Mechanical Engineering National Institute of Technology Rourkela 769008 India.
Abstract
Recent studies reveal that polyster matrix composites reinforced by ceramic fillers have significantly better characteristics such as super wear resistance, high strength and low density than unreinforced materials. However, prohibitive costs and stability of properties pose challenge for the researchers in the process of development of composites. To address these issues, composites are being developed using waste materials as reinforcement for effective utilization of industrial wastes. The present investigation aims to develop redmud filled polyster composites with different weight fraction and characterize mechanical and tribological properties. The engineering application of composites demands that it should have high wear resistance, low density and high tensile strength. In order to assess the behavior of composites satisfying multiple performance measures, a grey-based Taguchi approach has been adopted. After thorough analysis of factors and their interactions, optimal factor settings have been suggested to improve multiple responses viz., specific wear rate, density and tensile strength. The responses have been predicted using both artificial neural network (ANN) and Taguchi method so that a comparative evaluation can be made.
Keywords
- grey-based Taguchi method
- neural networks
- sliding wear
- red mud
- polyester
How to Cite
Siba Sankar Mahapatra and Saurav Datta, "A Grey-based Taguchi Method for Wear Assesment of Red Mud Filled Polyester Composites," International Journal of Modeling and Optimization, vol. 1, no. 1, pp. 80-88, 2011. https://doi.org/ 10.7763/IJMO.2011.V1.15
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).