Abstract
The investigation of tribological behaviour of AA7178 base alloy matrix reinforced with varying weight percentage of nano TiO2 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 TiO2 by 60.95% and 57.33%, respectively. The efficiency of ANN model was better compared to Taguchi model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
