Experimental Investigation and Prediction of Tribological Properties of Titanium Carbide and Multiwalled Carbon Nanotubes Reinforced Aluminium Composites using Artificial Neural Network

Mohamed Zakaulla, Anteneh Mohammed Tahir, Seid Endro, Shemelis Nesibu Wodaeneh and Lulseged Belay
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Mohamed Zakaulla: Kombolcha Institute of Technology, Department of Mechanical Engineering, Wollo University, Kombolcha, Ethiopia
Anteneh Mohammed Tahir: Kombolcha Institute of Technology, Department of Mechanical Engineering, Wollo University, Kombolcha, Ethiopia
Seid Endro: Kombolcha Institute of Technology, Department of Mechanical Engineering, Wollo University, Kombolcha, Ethiopia
Shemelis Nesibu Wodaeneh: Kombolcha Institute of Technology, Department of Mechanical Engineering, Wollo University, Kombolcha, Ethiopia
Lulseged Belay: Kombolcha Institute of Technology, Department of Mechanical Engineering, Wollo University, Kombolcha, Ethiopia

European Journal of Engineering and Technology Research, 2018, vol. 3, issue 8, 40-43

Abstract: In this study, the tribological properties of TiC particle and MWCNTs reinforced aluminium (Al7475) hybrid composite synthesized by stir casting method were investigated by experimental and artificial neural network (ANN) model. Al7475 metal matrix composites was produced with different wt% of TiC and MWCNTs. The composite samples were tested at 0.42 ms- 1, 0.84 ms- 1 and 1.68 ms- 1 under three different loads (10N, 20N and 40N). The results indicated that Al7475+10%TiC+2%MWCNTs composite exhibit lower wear rate and reduced coefficient of friction in compare to other samples. TiC percent, MWCNTs percent, applied weight, sliding speed and Time were used as input values for the theoretical prediction model of the composite. coefficient of friction and Wear loss were the two outputs developed from proposed network. Back propagation neural network with 5 – 6 – 2 architecture that uses Levenberg –Marquardt training algorithm is used to predict the coefficient of friction and wear loss. After comparing experimental and ANNs predicted results it was noted that R2 was 0.992 for wear loss and 0.980 for coefficient of friction. This indicated that developed predicted model has a high state of reliability.

Keywords: Artificial Neural Network; Composites Friction; Wear (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:3:y:2018:i:8:id:60834

DOI: 10.24018/ejeng.2018.3.8.834

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