Artificial Neural Network Prediction On Wear Of Al6061 Alloy Metal Matrix Composites Reinforced With -Al2o3. (2018)

• Record Type: Journal Article
• Authors: Veeresh Kumar, G.B.; Pramod, R.; Rao, C.S.P.; Gouda, P.S. Shivakumar
• Is Part Of: Materials today. Volume 5:Number 5(2018)Part 2
• Page Start: 11268
• Page End: 11276
• Publication Date: 2018
• Subjects: Metal Matrix Composites -- Al6061 -- Al2O3 -- Sliding Wear -- Artificial Neural Networks; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2018.02.093
• Languages: English
• ISSNs: 2214-7853

Abstract

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, Aluminium Oxide (Al2 03 ) 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-Al2 O3 filled composites. The Al6061 with 6 wt% Al2 O3 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.