Artificial Neural Networks for the Prediction of Wear Properties of Al6061-TiO2 Composites

International Conference on Materials, Alloys and Experimental Mechanics 2017

材料科学;金属学;机械制造

Veeresh Kumar, G.B.^1 ; Pramod, R.^1 ; Shivakumar Gouda, P.S.^2 ; Rao, C.S.P.^3

Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Bengaluru, India^1

Department of Mechanical Engineering, SDM College of Engineering and Technology Visvesvaraya, Technological University, Dharwad, India^2

Department of Mechanical Engineering, National Institute of Technology, Warangal, India^3

关键词: Automotive component;    Filled composites;    Monolithic material;    Non-linear relationships;    Reinforcement materials;    Sliding distances;    Titanium dioxides (TiO2);    Weight percentages;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/225/1/012046/pdf
DOI  :  10.1088/1757-899X/225/1/012046

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

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-TiO2filled composites. The Al6061 with 3 wt% TiO2composite 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.