Optimization of tribological behaviour of TiO 2 nanoparticles reinforced AA7178 alloy matrix using ANN and Taguchi’s methodology

The investigation of tribological behaviour of AA7178 base alloy matrix reinforced with varying weight percentage of nano TiO 2 particles (0,1,2 and 3%) using artificial neural network (ANN) and Taguchi is presented in this paper. Scanning Electron Microscope(SEM) with Energy Dispersive Spectroscopy...

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Bibliographic Details
Published in:Surface topography metrology and properties 2022-06, Vol.10 (2), p.25032
Main Authors: Bharat, Nikhil, Bose, P S C
Format: Article
Language:English
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Summary:The investigation of tribological behaviour of AA7178 base alloy matrix reinforced with varying weight percentage of nano TiO 2 particles (0,1,2 and 3%) using artificial neural network (ANN) and Taguchi is presented in this paper. Scanning Electron Microscope(SEM) with Energy Dispersive Spectroscopy (EDAX) was used to study the microstructural as well as wear behaviour of the nanocomposite. SEM images confirm that abrasive and adhesive wear was responsible for the worn-out surface. ANN with the Taguchi model was used to obtain the best input process parameters (sliding speed, distance, load and weight percentage) to minimize the output values (Coefficient of friction and wear rate). The coefficient of friction and wear rate were mainly affected from the weight percentage of nano TiO 2 by 60.95% and 57.33%, respectively. The efficiency of ANN model was better compared to Taguchi model.
ISSN:2051-672X
2051-672X
DOI:10.1088/2051-672X/ac7a55