Prediction of tribological characteristics of powder metallurgy Ti and W added low alloy steels using artificial neural network
In the present research work, the effects of Titanium (Ti) and Tungsten (W) addition on tribological behavior of powdermetallurgy (P/M) Fe-1%C steel have been investigated. The test specimens of plain carbon steel and 1%Ti, 1%W and1%Ti+1%W added plain carbon steels were used to conduct the wear tests and wear behavior analyses. The optical andSEM images of wear tracks and microstructures of the alloys were obtained and analysed with wear behavior of the alloysteels. Artificial Neural Network (ANN) software was used to check the degree of agreement of test results with predictedvalues. The experimental results show that Ti and W added alloy steel exhibits excellent wear resistance. The carbidesformation due to alloying elements pronounces the wear resistance of the alloy steel. It has been proven that ANN could beused as a tool to predict the wear behavior of the P/M alloy steels by agreement between the predicted and experimentalvalues.
Wear, Mass loss, P/M alloy steels, Friction co-efficient, Artificial neural network
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