Publication Type:

Conference Paper

Source:

IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Volume 225, Number 1 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030319868&doi=10.1088%2f1757-899X%2f225%2f1%2f012046&partnerID=40&md5=864b12538f6a5b63a258d39bb6a124eb

Keywords:

Aluminum, Aluminum alloys, Automotive component, Backpropagation, Filled composites, Metallic matrix composites, Monolithic material, Neural networks, Non-linear relationships, Reinforcement, Reinforcement materials, Sliding distances, Titanium dioxides (TiO2), Wear of materials, Wear resistance, Weight percentages

Abstract:

The exceptional performance of composite materials in comparison with the monolithic materials have been extensively studied by researchers. Among the metal matrix composites Aluminium matrix based composites have displayed superior mechanical properties. The aluminium 6061 alloy has been used in aeronautical and automotive components, but their resistance against the wear is poor. To enhance the wear properties, Titanium dioxide (TiO2) particulates have been used as reinforcements. In the present investigation Back propagation (BP) technique has been adopted for Artificial Neural Network [ANN] modelling. The wear experimentations were carried out on a pin-on-disc wear monitoring apparatus. For conduction of wear tests ASTM G99 was adopted. Experimental design was carried out using Taguchi L27 orthogonal array. The sliding distance, weight percentage of the reinforcement material and applied load have a substantial influence on the height damage due to wear of the Al6061 and Al6061-TiO2 filled composites. The Al6061 with 3 wt% TiO2 composite displayed an excellent wear resistance in comparison with other composites investigated. A non-linear relationship between density, applied load, weight percentage of reinforcement, sliding distance and height decrease due to wear has been established using an artificial neural network. A good agreement has been observed between experimental and ANN model predicted results. © Published under licence by IOP Publishing Ltd.

Notes:

cited By 0; Conference of 1st International Conference on Materials, Alloys and Experimental Mechanics, ICMAEM 2017 ; Conference Date: 3 July 2017 Through 4 July 2017; Conference Code:130336

Cite this Research Publication

G. B. Veeresh Kumar, Pramod, R., Gouda, P. S. Shivakumar, and Rao, C. S. P., “Artificial Neural Networks for the Prediction of Wear Properties of Al6061-TiO2 Composites”, in IOP Conference Series: Materials Science and Engineering, 2017, vol. 225.

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